# This script generates onnx files for MIGraphX onnx operator tests. # To generate an individual onnx file, you can use the following # command: python -c "import gen_onnx; gen_onnx.{test_name}_test()" import numpy as np import onnx from onnx import helper from onnx import TensorProto def onnx_test(op_test): def run_test(): op_info = op_test() if len(op_info) > 3: graph_def = helper.make_graph(op_info[0], op_test.__name__, op_info[1], op_info[2], initializer=op_info[3]) else: graph_def = helper.make_graph(op_info[0], op_test.__name__, op_info[1], op_info[2]) model_def = helper.make_model(graph_def, producer_name=op_test.__name__) onnx.save(model_def, '{}.onnx'.format(op_test.__name__)) return run_test @onnx_test def acos_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10]) node = onnx.helper.make_node( 'Acos', inputs=['x'], outputs=['y'], ) return ([node], [x], [y]) @onnx_test def acosh_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10]) node = onnx.helper.make_node( 'Acosh', inputs=['x'], outputs=['y'], ) return ([node], [x], [y]) @onnx_test def add_bcast_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4]) z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [2, 3, 4, 5]) node = onnx.helper.make_node('Add', inputs=['0', '1'], broadcast=1, axis=1, outputs=['2']) return ([node], [x, y], [z]) @onnx_test def add_fp16_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT16, [1]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT16, [1]) z = helper.make_tensor_value_info('2', TensorProto.FLOAT16, [1]) node = onnx.helper.make_node( 'Add', inputs=['0', '1'], outputs=['2'], ) return ( [node], [x, y], [z], # '0' -> 1.5, '1' -> 2.5 [ onnx.helper.make_tensor('0', TensorProto.FLOAT16, [1], [15872]), onnx.helper.make_tensor('1', TensorProto.FLOAT16, [1], [16640]) ]) @onnx_test def add_scalar_test(): x = helper.make_tensor_value_info('0', TensorProto.UINT8, [2, 3, 4, 5]) y = helper.make_tensor_value_info('1', TensorProto.UINT8, []) z = helper.make_tensor_value_info('2', TensorProto.UINT8, [2, 3, 4, 5]) node = onnx.helper.make_node('Add', inputs=['0', '1'], outputs=['2']) return ([node], [x, y], [z]) @onnx_test def argmax_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 6]) node = onnx.helper.make_node('ArgMax', inputs=['x'], outputs=['y'], axis=2, keepdims=0) return ([node], [x], [y]) @onnx_test def argmin_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 5]) node = onnx.helper.make_node('ArgMin', inputs=['x'], outputs=['y'], axis=3, keepdims=0) return ([node], [x], [y]) @onnx_test def asin_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10]) node = onnx.helper.make_node( 'Asin', inputs=['x'], outputs=['y'], ) return ([node], [x], [y]) @onnx_test def asinh_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10]) node = onnx.helper.make_node( 'Asinh', inputs=['x'], outputs=['y'], ) return ([node], [x], [y]) @onnx_test def atan_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10]) node = onnx.helper.make_node( 'Atan', inputs=['x'], outputs=['y'], ) return ([node], [x], [y]) @onnx_test def atanh_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10]) node = onnx.helper.make_node( 'Atanh', inputs=['x'], outputs=['y'], ) return ([node], [x], [y]) @onnx_test def averagepool_1d_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 5]) out = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 3]) node = onnx.helper.make_node('AveragePool', inputs=['0'], outputs=['1'], kernel_shape=[3]) return ([node], [x], [out]) @onnx_test def averagepool_3d_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 5, 5, 5]) out = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 3, 3, 3]) node = onnx.helper.make_node('AveragePool', inputs=['0'], outputs=['1'], kernel_shape=[3, 3, 3]) return ([node], [x], [out]) @onnx_test def averagepool_notset_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 1, 1]) node = onnx.helper.make_node('AveragePool', inputs=['x'], outputs=['y'], kernel_shape=[6, 6], strides=[2, 2], pads=[0, 0, 1, 1], auto_pad='NOTSET') return ([node], [x], [y]) @onnx_test def averagepool_nt_cip_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 1, 1]) node = onnx.helper.make_node('AveragePool', inputs=['x'], outputs=['y'], kernel_shape=[6, 6], strides=[2, 2], pads=[0, 0, 1, 1], auto_pad='NOTSET', count_include_pad=1) return ([node], [x], [y]) @onnx_test def averagepool_same_lower_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 5, 5]) node = onnx.helper.make_node('AveragePool', inputs=['x'], outputs=['y'], kernel_shape=[2, 2], auto_pad='SAME_LOWER') return ([node], [x], [y]) @onnx_test def averagepool_sl_cip_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 5, 5]) node = onnx.helper.make_node('AveragePool', inputs=['x'], outputs=['y'], kernel_shape=[2, 2], auto_pad='SAME_LOWER', count_include_pad=1) return ([node], [x], [y]) @onnx_test def averagepool_same_upper_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 5, 5]) node = onnx.helper.make_node('AveragePool', inputs=['x'], outputs=['y'], kernel_shape=[2, 2], auto_pad='SAME_UPPER') return ([node], [x], [y]) @onnx_test def batchnorm_1d_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 5]) scale = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3]) bias = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3]) mean = helper.make_tensor_value_info('3', TensorProto.FLOAT, [3]) var = helper.make_tensor_value_info('4', TensorProto.FLOAT, [3]) out = helper.make_tensor_value_info('5', TensorProto.FLOAT, [1, 3, 5]) node = onnx.helper.make_node('BatchNormalization', inputs=['0', '1', '2', '3', '4'], outputs=['5'], epsilon=1e-6, momentum=0.9) return ([node], [x, scale, bias, mean, var], [out]) @onnx_test def batchnorm_3d_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 5, 5, 5]) scale = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3]) bias = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3]) mean = helper.make_tensor_value_info('3', TensorProto.FLOAT, [3]) var = helper.make_tensor_value_info('4', TensorProto.FLOAT, [3]) out = helper.make_tensor_value_info('5', TensorProto.FLOAT, [1, 3, 5, 5, 5]) node = onnx.helper.make_node('BatchNormalization', inputs=['0', '1', '2', '3', '4'], outputs=['5'], epsilon=1e-6, momentum=0.9) return ([node], [x, scale, bias, mean, var], [out]) @onnx_test def cast_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT16, [10]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10]) node = onnx.helper.make_node('Cast', inputs=['x'], outputs=['y'], to=1) return ([node], [x], [y]) @onnx_test def ceil_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10]) node = onnx.helper.make_node( 'Ceil', inputs=['x'], outputs=['y'], ) return ([node], [x], [y]) @onnx_test def clip_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3]) node = onnx.helper.make_node('Clip', inputs=['0'], outputs=['1'], max=6.0, min=0.0) return ([node], [x], [y]) @onnx_test def clip_test_op11(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3]) min_val = helper.make_tensor('min', TensorProto.FLOAT, [], [0.0]) max_val = helper.make_tensor('max', TensorProto.FLOAT, [], [6.0]) node = onnx.helper.make_node('Clip', inputs=['0', 'min', 'max'], outputs=['1']) return ([node], [x], [y], [min_val, max_val]) @onnx_test def clip_test_op11_max_only(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3]) max_val = helper.make_tensor('max', TensorProto.FLOAT, [], [0.0]) node = onnx.helper.make_node('Clip', inputs=['0', '', 'max'], outputs=['1']) return ([node], [x], [y], [max_val]) @onnx_test def clip_test_op11_min_only(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3]) min_val = helper.make_tensor('min', TensorProto.FLOAT, [], [0.0]) node = onnx.helper.make_node('Clip', inputs=['0', 'min'], outputs=['1']) return ([node], [x], [y], [min_val]) @onnx_test def clip_test_op11_no_args(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3]) node = onnx.helper.make_node('Clip', inputs=['0'], outputs=['1']) return ([node], [x], [y]) @onnx_test def clip_test_op11_no_args1(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3]) node = onnx.helper.make_node('Clip', inputs=['0', '', ''], outputs=['1']) return ([node], [x], [y]) @onnx_test def concat_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 4, 3]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7, 4, 3]) z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [9, 4, 3]) node = onnx.helper.make_node( 'Concat', inputs=['0', '1'], axis=0, outputs=['2'], ) return ([node], [x, y], [z]) @onnx_test def constant_test(): x = np.array([0, 1, 2]) y = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3]) node = onnx.helper.make_node( 'Constant', inputs=[], outputs=['0'], value=onnx.helper.make_tensor( name='const_tensor', data_type=TensorProto.FLOAT, dims=x.shape, vals=x.flatten().astype(float), ), ) return ([node], [], [y]) @onnx_test def constant_fill_test(): value = helper.make_tensor_value_info('value', TensorProto.FLOAT, [2, 3]) node = onnx.helper.make_node( 'ConstantFill', inputs=[], outputs=['value'], dtype=1, value=1.0, shape=[2, 3], input_as_shape=0, ) return ([node], [], [value]) @onnx_test def constant_fill_input_as_shape_test(): np_shape = np.array([2, 3]) value = helper.make_tensor_value_info('value', TensorProto.FLOAT, [2, 3]) ts_shape = helper.make_tensor(name='shape_tensor', data_type=TensorProto.INT32, dims=np_shape.shape, vals=np_shape.flatten().astype(int)) const_shape_node = onnx.helper.make_node( 'Constant', inputs=[], outputs=['shape'], value=ts_shape, ) node = onnx.helper.make_node( 'ConstantFill', inputs=['shape'], outputs=['value'], dtype=1, value=1.0, input_as_shape=1, ) return ([const_shape_node, node], [], [value]) @onnx_test def constant_scalar_test(): x = np.array([1]) y = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1]) node = onnx.helper.make_node( 'Constant', inputs=[], outputs=['0'], value=onnx.helper.make_tensor( name='const_tensor', data_type=TensorProto.INT32, dims=x.shape, vals=x.flatten().astype(int), ), ) return ([node], [], [y]) @onnx_test def const_of_shape_empty_input_test(): tensor_val = onnx.helper.make_tensor('value', onnx.TensorProto.INT64, [1], [10]) empty_val = np.array([]).astype(np.int64) empty_ts = helper.make_tensor(name='empty_tensor', data_type=TensorProto.INT32, dims=empty_val.shape, vals=empty_val.flatten().astype(int)) shape_const = helper.make_node( 'Constant', inputs=[], outputs=['shape'], value=empty_ts, ) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4]) node = onnx.helper.make_node( 'ConstantOfShape', inputs=['shape'], outputs=['y'], value=tensor_val, ) return ([shape_const, node], [], [y]) @onnx_test def const_of_shape_float_test(): tensor_val = onnx.helper.make_tensor('value', onnx.TensorProto.FLOAT, [1], [10]) shape_val = np.array([2, 3, 4]).astype(np.int64) shape_ts = helper.make_tensor(name='shape_tensor', data_type=TensorProto.INT32, dims=shape_val.shape, vals=shape_val.flatten().astype(int)) shape_const = helper.make_node( 'Constant', inputs=[], outputs=['shape'], value=shape_ts, ) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4]) node = onnx.helper.make_node('ConstantOfShape', inputs=['shape'], outputs=['y'], value=tensor_val) return ([shape_const, node], [], [y]) @onnx_test def const_of_shape_int64_test(): tensor_val = onnx.helper.make_tensor('value', onnx.TensorProto.INT64, [1], [10]) shape_val = np.array([2, 3, 4]).astype(np.int64) shape_ts = helper.make_tensor(name='shape_tensor', data_type=TensorProto.INT32, dims=shape_val.shape, vals=shape_val.flatten().astype(int)) shape_const = helper.make_node( 'Constant', inputs=[], outputs=['shape'], value=shape_ts, ) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4]) node = onnx.helper.make_node('ConstantOfShape', inputs=['shape'], outputs=['y'], value=tensor_val) return ([shape_const, node], [], [y]) @onnx_test def const_of_shape_no_value_attr_test(): shape_val = np.array([2, 3, 4]).astype(np.int64) shape_ts = helper.make_tensor(name='shape_tensor', data_type=TensorProto.INT32, dims=shape_val.shape, vals=shape_val.flatten().astype(int)) shape_const = helper.make_node( 'Constant', inputs=[], outputs=['shape'], value=shape_ts, ) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4]) node = onnx.helper.make_node( 'ConstantOfShape', inputs=['shape'], outputs=['y'], ) return ([shape_const, node], [], [y]) @onnx_test def conv_1d_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 5]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 3]) out = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 3]) node = onnx.helper.make_node('Conv', inputs=['0', '1'], outputs=['2']) return ([node], [x, y], [out]) @onnx_test def conv_3d_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 5, 5, 5]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 3, 3, 3]) out = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 3, 3, 3]) node = onnx.helper.make_node('Conv', inputs=['0', '1'], outputs=['2']) return ([node], [x, y], [out]) @onnx_test def conv_attr_fail_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 5]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 3]) out = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 3]) node = onnx.helper.make_node('Conv', inputs=['0', '1'], strides=[1, 1], outputs=['2']) return ([node], [x, y], [out]) @onnx_test def conv_autopad_fail_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 1, 1]) out = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 34, 34]) node = onnx.helper.make_node('Conv', inputs=['0', '1'], outputs=['2'], dilations=[1, 1], strides=[1, 1], auto_pad='SAME', pads=[0, 0, 1, 1, 0, 0, 1, 1]) return ([node], [x, y], [out]) @onnx_test def conv_autopad_same_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 3, 3]) out = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 32, 32]) node = onnx.helper.make_node('Conv', inputs=['0', '1'], outputs=['2'], dilations=[1, 1], strides=[1, 1], auto_pad='SAME') return ([node], [x, y], [out]) @onnx_test def conv_bias_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 5, 5]) z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1]) out = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 2, 28, 28]) node = onnx.helper.make_node('Conv', inputs=['0', '1', '2'], outputs=['3'], dilations=[1, 1], strides=[1, 1]) return ([node], [x, y, z], [out]) @onnx_test def conv_bn_relu_maxpool_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 5, 5]) z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1]) m = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1]) n = helper.make_tensor_value_info('4', TensorProto.FLOAT, [1]) k = helper.make_tensor_value_info('5', TensorProto.FLOAT, [1]) l = helper.make_tensor_value_info('6', TensorProto.FLOAT, [1]) out = helper.make_tensor_value_info('10', TensorProto.FLOAT, [1, 1, 14, 14]) node0 = onnx.helper.make_node('Conv', inputs=['0', '1', '2'], outputs=['7'], dilations=[1, 1], strides=[1, 1], pads=[0, 0, 0, 0]) node1 = onnx.helper.make_node('BatchNormalization', inputs=['7', '3', '4', '5', '6'], outputs=['8'], epsilon=9.99999974737875e-06, momentum=0.899999976158142) node2 = onnx.helper.make_node('Relu', inputs=['8'], outputs=['9']) node3 = onnx.helper.make_node('MaxPool', inputs=['9'], outputs=['10'], pads=[0, 0, 0, 0], strides=[2, 2], kernel_shape=[2, 2]) return ([node0, node1, node2, node3], [x, y, z, m, n, k, l], [out]) @onnx_test def conv_relu_maxpool_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 5, 5]) z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1]) out = helper.make_tensor_value_info('5', TensorProto.FLOAT, [1, 1, 14, 14]) node1 = onnx.helper.make_node('Conv', inputs=['0', '1', '2'], outputs=['3'], dilations=[1, 1], strides=[1, 1], pads=[0, 0, 0, 0]) node2 = onnx.helper.make_node('Relu', inputs=['3'], outputs=['4']) node3 = onnx.helper.make_node('MaxPool', inputs=['4'], outputs=['5'], pads=[0, 0, 0, 0], strides=[2, 2], kernel_shape=[2, 2]) return ([node1, node2, node3], [x, y, z], [out]) @onnx_test def conv_relu_maxpool_x2_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [5, 3, 5, 5]) z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [5]) m = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 5, 5, 5]) n = helper.make_tensor_value_info('4', TensorProto.FLOAT, [1]) out = helper.make_tensor_value_info('10', TensorProto.FLOAT, [1, 1, 5, 5]) node1 = onnx.helper.make_node('Conv', inputs=['0', '1', '2'], outputs=['5'], dilations=[1, 1], strides=[1, 1], pads=[0, 0, 0, 0]) node2 = onnx.helper.make_node('Relu', inputs=['5'], outputs=['6']) node3 = onnx.helper.make_node('MaxPool', inputs=['6'], outputs=['7'], pads=[0, 0, 0, 0], strides=[2, 2], kernel_shape=[2, 2]) node4 = onnx.helper.make_node('Conv', inputs=['7', '3', '4'], outputs=['8'], dilations=[1, 1], strides=[1, 1], pads=[0, 0, 0, 0]) node5 = onnx.helper.make_node('Relu', inputs=['8'], outputs=['9']) node6 = onnx.helper.make_node('MaxPool', inputs=['9'], outputs=['10'], pads=[0, 0, 0, 0], strides=[2, 2], kernel_shape=[2, 2]) return ([node1, node2, node3, node4, node5, node6], [x, y, z, m, n], [out]) @onnx_test def convinteger_bias_test(): x = helper.make_tensor_value_info('0', TensorProto.INT8, [1, 3, 32, 32]) y = helper.make_tensor_value_info('1', TensorProto.INT8, [1, 3, 5, 5]) z = helper.make_tensor_value_info('2', TensorProto.INT32, [1]) out = helper.make_tensor_value_info('3', TensorProto.INT32, [1, 2, 28, 28]) node = onnx.helper.make_node('ConvInteger', inputs=['0', '1', '2'], outputs=['3'], dilations=[1, 1], strides=[1, 1]) return ([node], [x, y, z], [out]) @onnx_test def cos_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10]) node = onnx.helper.make_node( 'Cos', inputs=['x'], outputs=['y'], ) return ([node], [x], [y]) @onnx_test def cosh_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1]) node = onnx.helper.make_node( 'Cosh', inputs=['x'], outputs=['y'], ) return ([node], [x], [y]) @onnx_test def deconv_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3]) w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 1, 3, 3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 5, 5]) node = onnx.helper.make_node('ConvTranspose', name='conv1', inputs=['x', 'w'], outputs=['y']) return ([node], [x, w], [y]) @onnx_test def deconv_bias_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3]) w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 1, 3, 3]) b = helper.make_tensor_value_info('b', TensorProto.FLOAT, [1]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 5, 5]) node = onnx.helper.make_node('ConvTranspose', name='conv1', inputs=['x', 'w', 'b'], outputs=['y']) return ([node], [x, w, b], [y]) @onnx_test def deconv_input_pads_strides_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3]) w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 2, 3, 3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 2, 7, 5]) node = onnx.helper.make_node('ConvTranspose', inputs=['x', 'w'], outputs=['y'], strides=[3, 2], pads=[1, 1, 1, 1]) return ([node], [x, w], [y]) @onnx_test def deconv_input_pads_asymm_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3]) w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 2, 3, 3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 2, 8, 6]) node = onnx.helper.make_node('ConvTranspose', inputs=['x', 'w'], outputs=['y'], strides=[3, 2], pads=[0, 0, 1, 1]) return ([node], [x, w], [y]) @onnx_test def deconv_input_pads_asymm_1d_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3]) w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 2, 3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 2, 6]) node = onnx.helper.make_node('ConvTranspose', inputs=['x', 'w'], outputs=['y'], strides=[2], pads=[0, 1], dilations=[1]) return ([node], [x, w], [y]) @onnx_test def deconv_output_padding_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3]) w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 2, 3, 3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 2, 10, 8]) node = onnx.helper.make_node('ConvTranspose', inputs=['x', 'w'], outputs=['y'], strides=[3, 2], output_padding=[1, 1]) return ([node], [x, w], [y]) @onnx_test def deconv_output_padding_3d_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3, 3]) w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 2, 3, 3, 3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 2, 10, 8, 8]) node = onnx.helper.make_node('ConvTranspose', inputs=['x', 'w'], outputs=['y'], strides=[3, 2, 2], output_padding=[1, 1, 1]) return ([node], [x, w], [y]) @onnx_test def deconv_output_shape_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3]) w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 2, 3, 3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 2, 10, 8]) node = onnx.helper.make_node('ConvTranspose', inputs=['x', 'w'], outputs=['y'], strides=[3, 2], output_shape=[10, 8]) return ([node], [x, w], [y]) @onnx_test def deconv_output_shape_3d_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3, 3]) w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 2, 3, 3, 3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 2, 10, 8, 8]) node = onnx.helper.make_node('ConvTranspose', inputs=['x', 'w'], outputs=['y'], strides=[3, 2, 2], output_shape=[10, 8, 8]) return ([node], [x, w], [y]) @onnx_test def deconv_stride_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3]) w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 2, 3, 3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 2, 7, 3]) node = onnx.helper.make_node('ConvTranspose', inputs=['x', 'w'], outputs=['y'], strides=[3, 2]) return ([node], [x, w], [y]) @onnx_test def depthtospace_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 8, 5, 5]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 2, 10, 10]) node = onnx.helper.make_node('DepthToSpace', inputs=['x'], outputs=['y'], blocksize=2, mode='DCR') return ([node], [x], [y]) @onnx_test def depthtospace_simple_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 8, 2, 3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 2, 4, 6]) node = onnx.helper.make_node('DepthToSpace', inputs=['x'], outputs=['y'], blocksize=2, mode='DCR') return ([node], [x], [y]) @onnx_test def depthtospace_crd_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 8, 5, 5]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 2, 10, 10]) node = onnx.helper.make_node('DepthToSpace', inputs=['x'], outputs=['y'], blocksize=2, mode='CRD') return ([node], [x], [y]) @onnx_test def spacetodepth_test(): x = helper.make_tensor_value_info('x', TensorProto.float, [2, 2, 10, 10]) y = helper.make_tensor_value_info('y', TensorProto.float, [2, 8, 5, 5]) node = onnx.helper.make_node('spacetodepth', inputs=['x'], outputs=['y'], blocksize=2) return ([node], [x], [y]) @onnx_test def spacetodepth_simple_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 2, 4, 6]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 8, 2, 3]) node = onnx.helper.make_node('SpaceToDepth', inputs=['x'], outputs=['y'], blocksize=2) return ([node], [x], [y]) @onnx_test def spacetodepth_invalid_blocksize_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 2, 4, 6]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 8, 2, 3]) node = onnx.helper.make_node('SpaceToDepth', inputs=['x'], outputs=['y'], blocksize=0.3) return ([node], [x], [y]) @onnx_test def spacetodepth_nondivisibility_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 2, 5, 5]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 8, 2, 2]) node = onnx.helper.make_node('SpaceToDepth', inputs=['x'], outputs=['y'], blocksize=2) return ([node], [x], [y]) @onnx_test def dequantizelinear_test(): arg0 = helper.make_tensor_value_info('0', TensorProto.INT8, [5]) arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1]) arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT, [5]) node = onnx.helper.make_node( 'DequantizeLinear', inputs=['0', '1'], outputs=['out'], ) return ([node], [arg0, arg1], [arg_out]) @onnx_test def dequantizelinear_zero_point_test(): arg0 = helper.make_tensor_value_info('0', TensorProto.INT8, [5]) arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1]) arg2 = helper.make_tensor_value_info('2', TensorProto.INT8, [1]) arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT, [5]) node = onnx.helper.make_node( 'DequantizeLinear', inputs=['0', '1', '2'], outputs=['out'], ) return ([node], [arg0, arg1, arg2], [arg_out]) def make_dequantizelinear_axis_graph(axis): arg0 = helper.make_tensor_value_info('0', TensorProto.INT8, [1, 1, 5, 1]) arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [5]) arg2 = helper.make_tensor_value_info('2', TensorProto.INT8, [5]) arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT, [1, 1, 5, 1]) node = onnx.helper.make_node('DequantizeLinear', inputs=['0', '1', '2'], outputs=['out'], axis=axis) return ([node], [arg0, arg1, arg2], [arg_out]) @onnx_test def dequantizelinear_axis_test(): return make_dequantizelinear_axis_graph(2) @onnx_test def dequantizelinear_neg_axis_test(): return make_dequantizelinear_axis_graph(-2) @onnx_test def dropout_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 2, 2]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 2, 2]) node = onnx.helper.make_node( 'Dropout', inputs=['0'], outputs=['1'], ) return ([node], [x], [y]) @onnx_test def elu_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3]) node = onnx.helper.make_node('Elu', inputs=['0'], outputs=['1'], alpha=0.01) return ([node], [x], [y]) @onnx_test def embedding_bag_test(): index_val = np.array([1, 0, 2]) offset_val = np.array([0]) index_tensor = helper.make_tensor(name='index_val', data_type=TensorProto.INT32, dims=index_val.shape, vals=index_val.astype(np.int32)) index = onnx.helper.make_node('Constant', inputs=[], outputs=['index'], value=index_tensor) offset_tensor = helper.make_tensor(name='offset_val', data_type=TensorProto.INT32, dims=offset_val.reshape(()).shape, vals=offset_val.astype(np.int32)) offset = onnx.helper.make_node('Constant', inputs=[], outputs=['offset'], value=offset_tensor) weight = helper.make_tensor_value_info('weight', TensorProto.FLOAT, [4, 2]) y1 = helper.make_tensor_value_info('y1', TensorProto.FLOAT, [1, 2]) y2 = helper.make_tensor_value_info('y2', TensorProto.FLOAT, [1, 2]) y3 = helper.make_tensor_value_info('y3', TensorProto.FLOAT, [1, 2]) node1 = onnx.helper.make_node('ATen', inputs=['weight', 'index', 'offset'], outputs=['y1'], mode=0, operator='embedding_bag') node2 = onnx.helper.make_node('ATen', inputs=['weight', 'index', 'offset'], outputs=['y2'], mode=1, operator='embedding_bag') node3 = onnx.helper.make_node('ATen', inputs=['weight', 'index', 'offset'], outputs=['y3'], mode=2, operator='embedding_bag') return ([index, offset, node1, node2, node3], [weight], [y1, y2, y3]) @onnx_test def embedding_bag_offset_test(): index_val = np.array([1, 0]) offset_val = np.array([0, 1]) index_tensor = helper.make_tensor(name='index_val', data_type=TensorProto.INT32, dims=index_val.shape, vals=index_val.astype(np.int32)) index = onnx.helper.make_node('Constant', inputs=[], outputs=['index'], value=index_tensor) offset_tensor = helper.make_tensor(name='offset_val', data_type=TensorProto.INT32, dims=offset_val.shape, vals=offset_val.astype(np.int32)) offset = onnx.helper.make_node('Constant', inputs=[], outputs=['offset'], value=offset_tensor) weight = helper.make_tensor_value_info('weight', TensorProto.FLOAT, [2, 3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3]) node = onnx.helper.make_node('ATen', inputs=['weight', 'index', 'offset'], outputs=['y'], mode=0, operator='embedding_bag') return ([index, offset, node], [weight], [y]) @onnx_test def equal_test(): ax1 = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) x1 = helper.make_tensor("x1", data_type=TensorProto.FLOAT, dims=(2, 3), vals=ax1.astype(np.float32)) x2 = helper.make_tensor_value_info('x2', TensorProto.FLOAT, [2, 3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3]) node = onnx.helper.make_node( 'Equal', inputs=['x1', 'x2'], outputs=['y'], ) return ([node], [x2], [y], [x1]) @onnx_test def equal_bool_test(): x1 = helper.make_tensor_value_info('x1', TensorProto.FLOAT, [2, 3]) x2 = helper.make_tensor_value_info('x2', TensorProto.BOOL, [2, 3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3]) node1 = onnx.helper.make_node('Cast', inputs=['x1'], outputs=['bx1'], to=9) node2 = onnx.helper.make_node( 'Equal', inputs=['bx1', 'x2'], outputs=['y'], ) return ([node1, node2], [x1, x2], [y]) @onnx_test def erf_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10, 15]) node = onnx.helper.make_node( 'Erf', inputs=['x'], outputs=['y'], ) return ([node], [x], [y]) @onnx_test def exp_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10]) node = onnx.helper.make_node( 'Exp', inputs=['x'], outputs=['y'], ) return ([node], [x], [y]) @onnx_test def expand_test(): shape_val = np.array([2, 3, 4, 5]).astype(np.int64) shape_ts = helper.make_tensor(name='shape_tensor', data_type=TensorProto.INT32, dims=shape_val.shape, vals=shape_val.flatten().astype(int)) shape_const = helper.make_node( 'Constant', inputs=[], outputs=['shape'], value=shape_ts, ) x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 1, 1]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4, 5]) node = onnx.helper.make_node('Expand', inputs=['x', 'shape'], outputs=['y']) return ([shape_const, node], [x], [y]) @onnx_test def flatten_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5]) y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [6, 20]) y2 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [2, 60]) node = onnx.helper.make_node('Flatten', inputs=['0'], axis=2, outputs=['2']) node2 = onnx.helper.make_node('Flatten', inputs=['0'], outputs=['3']) return ([node, node2], [x], [y, y2]) @onnx_test def flatten_nonstd_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 5, 4]) y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [6, 20]) y2 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [2, 60]) trans = helper.make_node( 'Transpose', inputs=['0'], outputs=['tx'], perm=[0, 1, 3, 2], ) node = onnx.helper.make_node('Flatten', inputs=['tx'], axis=2, outputs=['2']) node2 = onnx.helper.make_node('Flatten', inputs=['tx'], outputs=['3']) return ([trans, node, node2], [x], [y, y2]) @onnx_test def floor_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10]) node = onnx.helper.make_node( 'Floor', inputs=['x'], outputs=['y'], ) return ([node], [x], [y]) @onnx_test def gather_test(): x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 4, 5, 6]) i = helper.make_tensor_value_info('indices', TensorProto.INT32, [2, 3, 4, 5]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4, 5]) node = onnx.helper.make_node( 'Gather', inputs=['data', 'indices'], outputs=['y'], axis=1, ) return ([node], [x, i], [y]) @onnx_test def gather_elements_axis0_test(): x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 4]) i = helper.make_tensor_value_info('indices', TensorProto.INT32, [2, 3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3]) node = onnx.helper.make_node( 'GatherElements', inputs=['data', 'indices'], outputs=['y'], axis=0, ) return ([node], [x, i], [y]) @onnx_test def gather_elements_axis1_test(): x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 4]) i = helper.make_tensor_value_info('indices', TensorProto.INT32, [2, 3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3]) node = onnx.helper.make_node( 'GatherElements', inputs=['data', 'indices'], outputs=['y'], axis=1, ) return ([node], [x, i], [y]) @onnx_test def gemm_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [5, 7]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [11, 5]) z = helper.make_tensor_value_info('2', TensorProto.FLOAT, []) a = helper.make_tensor_value_info('3', TensorProto.FLOAT, [7, 11]) node = onnx.helper.make_node('Gemm', inputs=['0', '1', '2'], outputs=['3'], alpha=2.0, beta=2.0, transA=1, transB=1) return ([node], [x, y, z], [a]) @onnx_test def gemm_ex_test(): m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 1, 8, 6]) m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 8, 7]) m3 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 1, 6, 7]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 6, 7]) node = onnx.helper.make_node('Gemm', inputs=['1', '2', '3'], outputs=['y'], alpha=0.5, beta=0.8, transA=1) return ([node], [m1, m2, m3], [y]) @onnx_test def gemm_ex_brcst_test(): m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 1, 5, 6]) m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 5, 7]) m3 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 1, 6, 1]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 6, 7]) node = onnx.helper.make_node('Gemm', inputs=['1', '2', '3'], outputs=['y'], alpha=0.5, beta=0.8, transA=1) return ([node], [m1, m2, m3], [y]) @onnx_test def gemm_half_test(): m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT16, [1, 1, 8, 6]) m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT16, [1, 1, 8, 7]) m3 = helper.make_tensor_value_info('3', TensorProto.FLOAT16, [1, 1, 6, 1]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT16, [1, 1, 6, 7]) node = onnx.helper.make_node('Gemm', inputs=['1', '2', '3'], outputs=['y'], alpha=0.5, beta=0.8, transA=1) return ([node], [m1, m2, m3], [y]) @onnx_test def globalavgpool_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 16, 16]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 1, 1]) node = onnx.helper.make_node( 'GlobalAveragePool', inputs=['0'], outputs=['1'], ) return ([node], [x], [y]) @onnx_test def globalmaxpool_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 16, 16]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 1, 1]) node = onnx.helper.make_node( 'GlobalMaxPool', inputs=['0'], outputs=['1'], ) return ([node], [x], [y]) @onnx_test def greater_test(): ax1 = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) x1 = helper.make_tensor("x1", data_type=TensorProto.FLOAT, dims=(2, 3), vals=ax1.astype(np.float32)) x2 = helper.make_tensor_value_info('x2', TensorProto.FLOAT, [2, 3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3]) node = onnx.helper.make_node( 'Greater', inputs=['x1', 'x2'], outputs=['y'], ) return ([node], [x2], [y], [x1]) @onnx_test def greater_bool_test(): x1 = helper.make_tensor_value_info('x1', TensorProto.FLOAT, [2, 3]) x2 = helper.make_tensor_value_info('x2', TensorProto.BOOL, [2, 3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3]) node1 = onnx.helper.make_node('Cast', inputs=['x1'], outputs=['bx1'], to=9) node2 = onnx.helper.make_node( 'Greater', inputs=['bx1', 'x2'], outputs=['y'], ) return ([node1, node2], [x1, x2], [y]) @onnx_test def greaterorequal_test(): x1 = helper.make_tensor_value_info('x1', TensorProto.FLOAT, [3]) x2 = helper.make_tensor_value_info('x2', TensorProto.FLOAT, [3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3]) node = onnx.helper.make_node( 'GreaterOrEqual', inputs=['x1', 'x2'], outputs=['y'], ) return ([node], [x1, x2], [y]) @onnx_test def group_conv_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 4, 16, 16]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 1, 3, 3]) z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 4, 14, 14]) node = onnx.helper.make_node( 'Conv', inputs=['0', '1'], group=4, outputs=['2'], ) return ([node], [x, y], [z]) @onnx_test def hardsigmoid_default_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 3, 4, 5]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 3, 4, 5]) node = onnx.helper.make_node('HardSigmoid', inputs=['x'], outputs=['y']) return ([node], [x], [y]) @onnx_test def hardsigmoid_double_test(): x = helper.make_tensor_value_info('x', TensorProto.DOUBLE, [1, 3, 4, 5]) y = helper.make_tensor_value_info('y', TensorProto.DOUBLE, [1, 3, 4, 5]) node = onnx.helper.make_node('HardSigmoid', inputs=['x'], outputs=['y'], alpha=0.3, beta=0.7) return ([node], [x], [y]) @onnx_test def hardsigmoid_half_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT16, [1, 3, 4, 5]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT16, [1, 3, 4, 5]) node = onnx.helper.make_node('HardSigmoid', inputs=['x'], outputs=['y']) return ([node], [x], [y]) @onnx_test def hardsigmoid_verify_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 5]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 5]) node = onnx.helper.make_node('HardSigmoid', inputs=['x'], outputs=['y']) return ([node], [x], [y]) @onnx_test def hardswish_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 5]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 5]) node = onnx.helper.make_node('HardSwish', inputs=['x'], outputs=['y']) return ([node], [x], [y]) @onnx_test def if_else_test(): x = onnx.helper.make_tensor_value_info('x', onnx.TensorProto.FLOAT, [2, 3]) y = onnx.helper.make_tensor_value_info('y', onnx.TensorProto.FLOAT, [2, 3]) then_out = onnx.helper.make_tensor_value_info('then_out', onnx.TensorProto.FLOAT, [2, 3]) else_out = onnx.helper.make_tensor_value_info('else_out', onnx.TensorProto.FLOAT, [2, 3]) xt = np.ones((2, 3)).astype(np.float) xt_tensor = helper.make_tensor(name='xt', data_type=TensorProto.FLOAT, dims=xt.shape, vals=xt.flatten().astype(np.float32)) yt = np.random.randn(2, 3).astype(np.float) yt_tensor = helper.make_tensor(name='yt', data_type=TensorProto.FLOAT, dims=yt.shape, vals=yt.flatten().astype(np.float32)) then_add_node = onnx.helper.make_node('Add', inputs=['x', 'xt'], outputs=['then_out']) else_mul_node = onnx.helper.make_node('Mul', inputs=['y', 'yt'], outputs=['else_out']) then_body = onnx.helper.make_graph([then_add_node], 'then_body', [], [then_out]) else_body = onnx.helper.make_graph([else_mul_node], 'else_body', [], [else_out]) cond = np.array([0]).astype(np.bool) cond_tensor = helper.make_tensor(name="cond", data_type=TensorProto.BOOL, dims=cond.shape, vals=cond.astype(bool)) res = onnx.helper.make_tensor_value_info('res', TensorProto.FLOAT, []) node = onnx.helper.make_node('If', inputs=['cond'], outputs=['res'], then_branch=then_body, else_branch=else_body) return ([node], [x, y], [res], [cond_tensor, xt_tensor, yt_tensor]) @onnx_test def if_literal_test(): then_out = onnx.helper.make_tensor_value_info('then_out', onnx.TensorProto.FLOAT, [5]) else_out = onnx.helper.make_tensor_value_info('else_out', onnx.TensorProto.FLOAT, [5]) x = np.array([1, 2, 3, 4, 5]).astype(np.float32) y = np.array([5, 4, 3, 2, 1]).astype(np.float32) z = np.array([]).astype(np.float32) then_const_node = onnx.helper.make_node( 'Constant', inputs=[], outputs=['then_out'], value=onnx.numpy_helper.from_array(x)) else_const_node = onnx.helper.make_node( 'Constant', inputs=[], outputs=['else_out'], value=onnx.numpy_helper.from_array(y)) empty_const_node = onnx.helper.make_node( 'Constant', inputs=[], outputs=['empty_out'], value=onnx.numpy_helper.from_array(z)) then_body = onnx.helper.make_graph([then_const_node, empty_const_node], 'then_body', [], [then_out]) else_body = onnx.helper.make_graph([else_const_node, empty_const_node], 'else_body', [], [else_out]) cond_input = onnx.helper.make_tensor_value_info('cond', onnx.TensorProto.BOOL, []) ret = onnx.helper.make_tensor_value_info('ret', TensorProto.FLOAT, []) node = onnx.helper.make_node('If', inputs=['cond'], outputs=['ret'], then_branch=then_body, else_branch=else_body) return ([node], [cond_input], [ret]) @onnx_test def if_param_excp_test(): then_out = onnx.helper.make_tensor_value_info('then_out', onnx.TensorProto.FLOAT, [2, 3]) else_out = onnx.helper.make_tensor_value_info('else_out', onnx.TensorProto.FLOAT, [2, 3]) x = onnx.helper.make_tensor_value_info('x', onnx.TensorProto.FLOAT, [2, 3]) y = onnx.helper.make_tensor_value_info('y', onnx.TensorProto.FLOAT, [2, 4]) yt = np.random.randn(2, 4).astype(np.float) xt = np.random.randn(2, 3).astype(np.float) xt_tensor = helper.make_tensor(name='xt', data_type=TensorProto.FLOAT, dims=xt.shape, vals=xt.flatten().astype(np.float32)) yt_tensor = helper.make_tensor(name='yt', data_type=TensorProto.FLOAT, dims=yt.shape, vals=yt.flatten().astype(np.float32)) then_add_node = onnx.helper.make_node('Add', inputs=['x', 'xt'], outputs=['then_out']) else_mul_node = onnx.helper.make_node('Mul', inputs=['y', 'yt'], outputs=['else_out']) then_body = onnx.helper.make_graph([then_add_node], 'then_body', [], [then_out], [xt_tensor]) else_body = onnx.helper.make_graph([else_mul_node], 'else_body', [], [else_out], [yt_tensor]) cond_input = onnx.helper.make_tensor_value_info('cond', onnx.TensorProto.BOOL, []) ret = onnx.helper.make_tensor_value_info('ret', TensorProto.FLOAT, []) node = onnx.helper.make_node('If', inputs=['cond'], outputs=['ret'], then_branch=then_body, else_branch=else_body) return ([node], [cond_input, x, y], [ret]) @onnx_test def if_param_excp1_test(): then_out = onnx.helper.make_tensor_value_info('sub_out', onnx.TensorProto.FLOAT, [2, 3]) x = onnx.helper.make_tensor_value_info('x', onnx.TensorProto.FLOAT, [2, 3]) xt = np.random.randn(2, 3).astype(np.float) xt_tensor = helper.make_tensor(name='xt', data_type=TensorProto.FLOAT, dims=xt.shape, vals=xt.flatten().astype(np.float32)) then_add_node = onnx.helper.make_node('Add', inputs=['x', 'xt'], outputs=['sub_out']) sub_body = onnx.helper.make_graph([then_add_node], 'sub_body', [], [then_out], [xt_tensor]) cond_input = onnx.helper.make_tensor_value_info('cond', onnx.TensorProto.BOOL, [2]) ret = onnx.helper.make_tensor_value_info('ret', TensorProto.FLOAT, []) node = onnx.helper.make_node('If', inputs=['cond'], outputs=['ret'], then_branch=sub_body, else_branch=sub_body) return ([node], [cond_input, x], [ret]) @onnx_test def if_param_test(): then_out = onnx.helper.make_tensor_value_info('then_out', onnx.TensorProto.FLOAT, [2, 3]) else_out = onnx.helper.make_tensor_value_info('else_out', onnx.TensorProto.FLOAT, [2, 3]) x = onnx.helper.make_tensor_value_info('x', onnx.TensorProto.FLOAT, [2, 3]) y = onnx.helper.make_tensor_value_info('y', onnx.TensorProto.FLOAT, [2, 3]) yt = np.random.randn(2, 3).astype(np.float) xt = np.random.randn(2, 3).astype(np.float) xt_tensor = helper.make_tensor(name='xt', data_type=TensorProto.FLOAT, dims=xt.shape, vals=xt.flatten().astype(np.float32)) yt_tensor = helper.make_tensor(name='yt', data_type=TensorProto.FLOAT, dims=yt.shape, vals=yt.flatten().astype(np.float32)) then_add_node = onnx.helper.make_node('Add', inputs=['x', 'xt'], outputs=['then_out']) else_mul_node = onnx.helper.make_node('Mul', inputs=['y', 'yt'], outputs=['else_out']) then_body = onnx.helper.make_graph([then_add_node], 'then_body', [], [then_out], [xt_tensor]) else_body = onnx.helper.make_graph([else_mul_node], 'else_body', [], [else_out], [yt_tensor]) cond_input = onnx.helper.make_tensor_value_info('cond', onnx.TensorProto.BOOL, []) ret = onnx.helper.make_tensor_value_info('ret', TensorProto.FLOAT, []) node = onnx.helper.make_node('If', inputs=['cond'], outputs=['ret'], then_branch=then_body, else_branch=else_body) return ([node], [cond_input, x, y], [ret]) @onnx_test def if_pl_test(): out_x = onnx.helper.make_tensor_value_info('out_x', onnx.TensorProto.FLOAT, [2, 3]) out_l_x = onnx.helper.make_tensor_value_info('out_l_x', onnx.TensorProto.FLOAT, [2, 3]) out_y = onnx.helper.make_tensor_value_info('out_y', onnx.TensorProto.FLOAT, [3, 3]) out_l_y = onnx.helper.make_tensor_value_info('out_l_y', onnx.TensorProto.FLOAT, [3, 3]) x = onnx.helper.make_tensor_value_info('x', onnx.TensorProto.FLOAT, [2, 3]) y = onnx.helper.make_tensor_value_info('y', onnx.TensorProto.FLOAT, [3, 3]) xt = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32) yt = np.array([[8, 7, 6], [5, 4, 3], [2, 1, 0]]).astype(np.float32) xt_tensor = helper.make_tensor(name='xt', data_type=TensorProto.FLOAT, dims=xt.shape, vals=xt.flatten().astype(np.float32)) yt_tensor = helper.make_tensor(name='yt', data_type=TensorProto.FLOAT, dims=yt.shape, vals=yt.flatten().astype(np.float32)) then_add_node = onnx.helper.make_node('Add', inputs=['x', 'xt'], outputs=['out_x']) else_mul_node = onnx.helper.make_node('Mul', inputs=['y', 'yt'], outputs=['out_y']) then_const_node = onnx.helper.make_node( 'Constant', inputs=[], outputs=['out_l_y'], value=onnx.numpy_helper.from_array(yt)) else_const_node = onnx.helper.make_node( 'Constant', inputs=[], outputs=['out_l_x'], value=onnx.numpy_helper.from_array(xt)) then_body = onnx.helper.make_graph([then_add_node, then_const_node], 'then_body', [], [out_x, out_l_y]) else_body = onnx.helper.make_graph([else_mul_node, else_const_node], 'else_body', [], [out_l_x, out_y]) cond_input = onnx.helper.make_tensor_value_info('cond', onnx.TensorProto.BOOL, []) ret = onnx.helper.make_tensor_value_info('ret', TensorProto.FLOAT, []) node = onnx.helper.make_node('If', inputs=['cond'], outputs=['ret'], then_branch=then_body, else_branch=else_body) return ([node], [cond_input, x, y], [ret], [xt_tensor, yt_tensor]) @onnx_test def if_then_test(): x = onnx.helper.make_tensor_value_info('x', onnx.TensorProto.FLOAT, [2, 3]) y = onnx.helper.make_tensor_value_info('y', onnx.TensorProto.FLOAT, [2, 3]) then_out = onnx.helper.make_tensor_value_info('then_out', onnx.TensorProto.FLOAT, [2, 3]) else_out = onnx.helper.make_tensor_value_info('else_out', onnx.TensorProto.FLOAT, [2, 3]) xt = np.ones((2, 3)).astype(np.float) xt_tensor = helper.make_tensor(name='xt', data_type=TensorProto.FLOAT, dims=xt.shape, vals=xt.flatten().astype(np.float32)) yt = np.random.randn(2, 3).astype(np.float) yt_tensor = helper.make_tensor(name='yt', data_type=TensorProto.FLOAT, dims=yt.shape, vals=yt.flatten().astype(np.float32)) then_add_node = onnx.helper.make_node('Add', inputs=['x', 'xt'], outputs=['then_out']) else_mul_node = onnx.helper.make_node('Mul', inputs=['y', 'yt'], outputs=['else_out']) then_body = onnx.helper.make_graph([then_add_node], 'then_body', [], [then_out]) else_body = onnx.helper.make_graph([else_mul_node], 'else_body', [], [else_out]) cond = np.array([1]).astype(np.bool) cond_tensor = helper.make_tensor(name="cond", data_type=TensorProto.BOOL, dims=cond.shape, vals=cond.astype(bool)) res = onnx.helper.make_tensor_value_info('res', TensorProto.FLOAT, []) node = onnx.helper.make_node('If', inputs=['cond'], outputs=['res'], then_branch=then_body, else_branch=else_body) return ([node], [x, y], [res], [cond_tensor, xt_tensor, yt_tensor]) @onnx_test def if_tuple_test(): x = onnx.helper.make_tensor_value_info('x', onnx.TensorProto.FLOAT, [1, 4]) y = onnx.helper.make_tensor_value_info('y', onnx.TensorProto.FLOAT, [3, 4]) cond_input = onnx.helper.make_tensor_value_info('cond', onnx.TensorProto.BOOL, []) then_out0 = onnx.helper.make_tensor_value_info('then_out0', onnx.TensorProto.FLOAT, [1, 4]) then_out1 = onnx.helper.make_tensor_value_info('then_out1', onnx.TensorProto.FLOAT, [3, 4]) else_out0 = onnx.helper.make_tensor_value_info('else_out0', onnx.TensorProto.FLOAT, [1, 4]) else_out1 = onnx.helper.make_tensor_value_info('else_out1', onnx.TensorProto.FLOAT, [3, 4]) one = np.ones([1]).astype(np.float) one_tensor = helper.make_tensor(name='one', data_type=TensorProto.FLOAT, dims=one.shape, vals=one.flatten().astype(np.float32)) two = np.array([2]).astype(np.float) two_tensor = helper.make_tensor(name='two', data_type=TensorProto.FLOAT, dims=two.shape, vals=two.flatten().astype(np.float32)) three = np.array([3]).astype(np.float) three_tensor = helper.make_tensor(name='three', data_type=TensorProto.FLOAT, dims=three.shape, vals=three.flatten().astype(np.float32)) then_add_node = onnx.helper.make_node('Add', inputs=['x', 'one'], outputs=['then_out0']) then_mul_node = onnx.helper.make_node('Mul', inputs=['y', 'two'], outputs=['then_out1']) else_mul_node = onnx.helper.make_node('Mul', inputs=['x', 'three'], outputs=['else_out0']) else_add_node = onnx.helper.make_node('Add', inputs=['y', 'three'], outputs=['else_out1']) then_body = onnx.helper.make_graph([then_add_node, then_mul_node], 'then_body', [], [then_out0, then_out1]) else_body = onnx.helper.make_graph([else_mul_node, else_add_node], 'else_body', [], [else_out0, else_out1]) res0 = onnx.helper.make_tensor_value_info('res0', TensorProto.FLOAT, []) res1 = onnx.helper.make_tensor_value_info('res1', TensorProto.FLOAT, []) node = onnx.helper.make_node('If', inputs=['cond'], outputs=['res0', 'res1'], then_branch=then_body, else_branch=else_body) return ([node], [cond_input, x, y], [res0, res1], [one_tensor, two_tensor, three_tensor]) @onnx_test def imagescaler_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 16, 16]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 16, 16]) node = onnx.helper.make_node('ImageScaler', inputs=['0'], outputs=['1'], bias=[0.01, 0.02, 0.03], scale=0.5) return ([node], [x], [y]) @onnx_test def imagescaler_half_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT16, [1, 3, 16, 16]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT16, [1, 3, 16, 16]) node = onnx.helper.make_node('ImageScaler', inputs=['0'], outputs=['1'], bias=[0.01, 0.02, 0.03], scale=0.5) return ([node], [x], [y]) @onnx_test def implicit_add_bcast_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4, 1]) z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [2, 3, 4, 5]) node = onnx.helper.make_node( 'Add', inputs=['0', '1'], outputs=['2'], ) return ([node], [x, y], [z]) @onnx_test def implicit_pow_bcast_test(): arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5]) arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4, 1]) arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT, [2, 3, 4, 5]) node = onnx.helper.make_node( 'Pow', inputs=['0', '1'], outputs=['out'], ) return ([node], [arg0, arg1], [arg_out]) @onnx_test def implicit_sub_bcast_test(): arg0 = helper.make_tensor_value_info('0', TensorProto.UINT64, [2, 3, 4, 5]) arg1 = helper.make_tensor_value_info('1', TensorProto.UINT64, [4, 5]) arg_out = helper.make_tensor_value_info('out', TensorProto.UINT64, [2, 3, 4, 5]) node = onnx.helper.make_node( 'Sub', inputs=['0', '1'], outputs=['out'], ) return ([node], [arg0, arg1], [arg_out]) @onnx_test def initializer_not_an_input(): values = np.array([[1, 2, 3, 4], [5, 6, 7, 8]]) w = helper.make_tensor(name='w', data_type=TensorProto.FLOAT, dims=values.shape, vals=values.flatten().astype(np.float)) x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [5, 2]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [5, 4]) node = onnx.helper.make_node( 'Gemm', inputs=['x', 'w'], outputs=['y'], ) return ([node], [x], [y], [w]) @onnx_test def instance_norm_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 2, 3, 3]) scale = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2]) bias = helper.make_tensor_value_info('2', TensorProto.FLOAT, [2]) y = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 2, 3, 3]) node = onnx.helper.make_node('InstanceNormalization', inputs=['0', '1', '2'], outputs=['3']) return ([node], [x, scale, bias], [y]) @onnx_test def instance_norm_val_test(): x = np.array([[[[0, 1, 2], [3, 4, 5], [6, 7, 8]], [[0, 1, 2], [3, 4, 5], [6, 7, 8]]]]) scale = np.array([1, 2]) bias = np.array([0, 1]) x_tensor = helper.make_tensor(name='x_tensor', data_type=TensorProto.FLOAT, dims=x.shape, vals=x.flatten().astype(np.float)) scale_tensor = helper.make_tensor(name='scale_tensor', data_type=TensorProto.FLOAT, dims=scale.shape, vals=scale.flatten().astype(np.float)) bias_tensor = helper.make_tensor(name='bias_tensor', data_type=TensorProto.FLOAT, dims=bias.shape, vals=bias.flatten().astype(np.float)) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 2, 3, 3]) node = onnx.helper.make_node( 'InstanceNormalization', inputs=['x_tensor', 'scale_tensor', 'bias_tensor'], outputs=['y']) return ([node], [], [y], [x_tensor, scale_tensor, bias_tensor]) @onnx_test def instance_norm_val_3d_test(): x = np.array([[[[[0, 1], [2, 3]], [[4, 5], [6, 7]]], [[[0, 1], [2, 3]], [[4, 5], [6, 7]]]]]) scale = np.array([1, 2]) bias = np.array([0, 1]) x_tensor = helper.make_tensor(name='x_tensor', data_type=TensorProto.FLOAT, dims=x.shape, vals=x.flatten().astype(np.float)) scale_tensor = helper.make_tensor(name='scale_tensor', data_type=TensorProto.FLOAT, dims=scale.shape, vals=scale.flatten().astype(np.float)) bias_tensor = helper.make_tensor(name='bias_tensor', data_type=TensorProto.FLOAT, dims=bias.shape, vals=bias.flatten().astype(np.float)) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 2, 2, 2, 2]) node = onnx.helper.make_node( 'InstanceNormalization', inputs=['x_tensor', 'scale_tensor', 'bias_tensor'], outputs=['y']) return ([node], [], [y], [x_tensor, scale_tensor, bias_tensor]) @onnx_test def layernorm_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 1, 5]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 1, 5]) scale = helper.make_tensor_value_info('scale', TensorProto.FLOAT, [5]) bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT, [5]) axes = [2] pow_2 = np.array([[[2, 2, 2, 2, 2]]]) epsilon = np.array([1e-12]) pow_tensor = helper.make_tensor(name='pow', data_type=TensorProto.FLOAT, dims=pow_2.shape, vals=pow_2.flatten().astype(np.float)) epsilon_tensor = helper.make_tensor(name='epsilon', data_type=TensorProto.FLOAT, dims=epsilon.shape, vals=epsilon.flatten().astype( np.float)) mean = onnx.helper.make_node('ReduceMean', inputs=['0'], outputs=['mean_out'], axes=axes) sub_mean = onnx.helper.make_node('Sub', inputs=['0', 'mean_out'], outputs=['sub_out']) sub_pow = onnx.helper.make_node('Pow', inputs=['sub_out', 'pow'], outputs=['pow_out']) var = onnx.helper.make_node('ReduceMean', inputs=['pow_out'], outputs=['var_out'], axes=axes) add = onnx.helper.make_node('Add', inputs=['var_out', 'epsilon'], outputs=['add_out']) sqrt = onnx.helper.make_node('Sqrt', inputs=['add_out'], outputs=['sqrt_out']) div = onnx.helper.make_node('Div', inputs=['sub_out', 'sqrt_out'], outputs=['div_out']) mul = onnx.helper.make_node('Mul', inputs=['scale', 'div_out'], outputs=['mul_out']) bias_add = onnx.helper.make_node('Add', inputs=['mul_out', 'bias'], outputs=['1']) return ([mean, sub_mean, sub_pow, var, add, sqrt, div, mul, bias_add], [x, scale, bias], [y], [pow_tensor, epsilon_tensor]) @onnx_test def leaky_relu_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3]) node = onnx.helper.make_node('LeakyRelu', inputs=['0'], outputs=['1'], alpha=0.01) return ([node], [x], [y]) @onnx_test def less_test(): ax1 = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) x1 = helper.make_tensor("x1", data_type=TensorProto.FLOAT, dims=(2, 3), vals=ax1.astype(np.float32)) x2 = helper.make_tensor_value_info('x2', TensorProto.FLOAT, [2, 3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3]) node = onnx.helper.make_node( 'Less', inputs=['x1', 'x2'], outputs=['y'], ) return ([node], [x2], [y], [x1]) @onnx_test def less_bool_test(): x1 = helper.make_tensor_value_info('x1', TensorProto.FLOAT, [2, 3]) x2 = helper.make_tensor_value_info('x2', TensorProto.BOOL, [2, 3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3]) node1 = onnx.helper.make_node('Cast', inputs=['x1'], outputs=['bx1'], to=9) node2 = onnx.helper.make_node( 'Less', inputs=['bx1', 'x2'], outputs=['y'], ) return ([node1, node2], [x1, x2], [y]) @onnx_test def lessorequal_test(): x1 = helper.make_tensor_value_info('x1', TensorProto.FLOAT, [3]) x2 = helper.make_tensor_value_info('x2', TensorProto.FLOAT, [3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3]) node = onnx.helper.make_node( 'LessOrEqual', inputs=['x1', 'x2'], outputs=['y'], ) return ([node], [x1, x2], [y]) @onnx_test def log_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10]) node = onnx.helper.make_node( 'Log', inputs=['x'], outputs=['y'], ) return ([node], [x], [y]) @onnx_test def logical_and_bcast_test(): x = helper.make_tensor_value_info('0', TensorProto.BOOL, [2, 3, 4, 5]) y = helper.make_tensor_value_info('1', TensorProto.BOOL, [4, 5]) z = helper.make_tensor_value_info('2', TensorProto.BOOL, [2, 3, 4, 5]) node = onnx.helper.make_node('And', inputs=['0', '1'], outputs=['2']) return ([node], [x, y], [z]) @onnx_test def logical_or_test(): x = helper.make_tensor_value_info('0', TensorProto.BOOL, [2, 3, 4, 5]) y = helper.make_tensor_value_info('1', TensorProto.BOOL, [2, 3, 4, 5]) z = helper.make_tensor_value_info('2', TensorProto.BOOL, [2, 3, 4, 5]) node = onnx.helper.make_node('Or', inputs=['0', '1'], outputs=['2']) return ([node], [x, y], [z]) @onnx_test def logical_xor_bcast_test(): x = helper.make_tensor_value_info('0', TensorProto.BOOL, [2, 3, 4, 5]) y = helper.make_tensor_value_info('1', TensorProto.BOOL, [4, 1]) z = helper.make_tensor_value_info('2', TensorProto.BOOL, [2, 3, 4, 5]) node = onnx.helper.make_node('Xor', inputs=['0', '1'], outputs=['2']) return ([node], [x, y], [z]) @onnx_test def logsoftmax_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 5, 6]) node = onnx.helper.make_node('LogSoftmax', inputs=['x'], outputs=['y'], axis=1) return ([node], [x], [y]) @onnx_test def logsoftmax_nonstd_input_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [6, 9]) y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3, 4]) node0 = onnx.helper.make_node('Slice', inputs=['0'], axes=[0, 1], starts=[1, 0], ends=[4, 4], outputs=['1']) node1 = onnx.helper.make_node('LogSoftmax', inputs=['1'], outputs=['2'], axis=-1) return ([node0, node1], [x], [y]) @onnx_test def loop_default_test(): body = helper.make_graph([ helper.make_node("Add", ["a", "b_in"], ["my_local"]), helper.make_node("Sub", ["a", "b_in"], ["a_sub_b_in"]), helper.make_node("Greater", ["my_local", "a_sub_b_in"], ["keep_going"]), helper.make_node("Add", ["a_sub_b_in", "a_sub_b_in"], ["user_defined_vals"]), ], "body", [ helper.make_tensor_value_info('iteration_num', TensorProto.INT64, []), helper.make_tensor_value_info('keep_going_inp', TensorProto.BOOL, []), helper.make_tensor_value_info('b_in', TensorProto.FLOAT, []) ], [ helper.make_tensor_value_info('keep_going', TensorProto.BOOL, []), helper.make_tensor_value_info('a_sub_b_in', TensorProto.FLOAT, []), helper.make_tensor_value_info('my_local', TensorProto.FLOAT, []), helper.make_tensor_value_info('user_defined_vals', TensorProto.FLOAT, []), ]) node = helper.make_node( "Loop", inputs=["", "", "b"], outputs=["b_loop", "my_local_loop", "user_defined_vals_loop"], body=body) a = helper.make_tensor_value_info('a', TensorProto.FLOAT, []) b = helper.make_tensor_value_info('b', TensorProto.FLOAT, []) b_loop = helper.make_tensor_value_info('b_loop', TensorProto.FLOAT, []) uout = helper.make_tensor_value_info('user_defined_vals_loop', TensorProto.FLOAT, [2, 1]) return ([node], [a, b], [b_loop, uout]) @onnx_test def loop_test(): body = helper.make_graph([ helper.make_node("Add", ["a", "b_in"], ["my_local"]), helper.make_node("Sub", ["a", "b_in"], ["a_sub_b_in"]), helper.make_node("Greater", ["my_local", "a_sub_b_in"], ["keep_going"]), helper.make_node("Add", ["a_sub_b_in", "a_sub_b_in"], ["user_defined_vals"]), ], "body", [ helper.make_tensor_value_info('iteration_num', TensorProto.INT64, [1]), helper.make_tensor_value_info('keep_going_inp', TensorProto.BOOL, [1]), helper.make_tensor_value_info('b_in', TensorProto.FLOAT, [1]) ], [ helper.make_tensor_value_info('keep_going', TensorProto.BOOL, [1]), helper.make_tensor_value_info('a_sub_b_in', TensorProto.FLOAT, [1]), helper.make_tensor_value_info('my_local', TensorProto.FLOAT, [1]), helper.make_tensor_value_info('user_defined_vals', TensorProto.FLOAT, [1]), ]) node = helper.make_node( "Loop", inputs=["max_trip_count", "keep_going_cond", "b"], outputs=["b_loop", "my_local_loop", "user_defined_vals_loop"], body=body) a = helper.make_tensor_value_info('a', TensorProto.FLOAT, [1]) b = helper.make_tensor_value_info('b', TensorProto.FLOAT, [1]) cond = helper.make_tensor_value_info('keep_going_cond', TensorProto.BOOL, [1]) iter = helper.make_tensor_value_info('max_trip_count', TensorProto.INT64, [1]) b_loop = helper.make_tensor_value_info('b_loop', TensorProto.FLOAT, [1]) uout = helper.make_tensor_value_info('user_defined_vals_loop', TensorProto.FLOAT, [2, 1]) return ([node], [iter, cond, a, b], [b_loop, uout]) @onnx_test def lrn_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 28, 24, 24]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 28, 24, 24]) node = onnx.helper.make_node('LRN', inputs=['0'], size=5, alpha=0.0001, beta=0.75, bias=1.0, outputs=['1']) return ([node], [x], [y]) @onnx_test def matmul_bmbm_test(): m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 6, 7]) m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [5, 2, 1, 7, 8]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [5, 2, 3, 6, 8]) node = onnx.helper.make_node( 'MatMul', inputs=['1', '2'], outputs=['y'], ) return ([node], [m1, m2], [y]) @onnx_test def matmul_bmv_test(): m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 6, 7]) m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 6]) node = onnx.helper.make_node( 'MatMul', inputs=['1', '2'], outputs=['y'], ) return ([node], [m1, m2], [y]) @onnx_test def matmul_mv_test(): m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [6, 7]) m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [6]) node = onnx.helper.make_node( 'MatMul', inputs=['1', '2'], outputs=['y'], ) return ([node], [m1, m2], [y]) @onnx_test def matmul_vbm_test(): m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7]) m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [5, 7, 8]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [5, 8]) node = onnx.helper.make_node( 'MatMul', inputs=['1', '2'], outputs=['y'], ) return ([node], [m1, m2], [y]) @onnx_test def matmul_vm_test(): m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7]) m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7, 8]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [8]) node = onnx.helper.make_node( 'MatMul', inputs=['1', '2'], outputs=['y'], ) return ([node], [m1, m2], [y]) @onnx_test def matmul_vv_test(): m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7]) m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1]) node = onnx.helper.make_node( 'MatMul', inputs=['1', '2'], outputs=['y'], ) return ([node], [m1, m2], [y]) @onnx_test def matmulinteger_test(): m1 = helper.make_tensor_value_info('1', TensorProto.INT8, [3, 6, 16]) m2 = helper.make_tensor_value_info('2', TensorProto.INT8, [3, 16, 8]) y = helper.make_tensor_value_info('y', TensorProto.INT32, [3, 6, 8]) node = onnx.helper.make_node( 'MatMulInteger', inputs=['1', '2'], outputs=['y'], ) return ([node], [m1, m2], [y]) @onnx_test def max_test(): a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3]) b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3]) c = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3]) y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3]) node = onnx.helper.make_node( 'Max', inputs=['0', '1', '2'], outputs=['3'], ) return ([node], [a, b, c], [y]) @onnx_test def maxpool_notset_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 1, 1]) node = onnx.helper.make_node('MaxPool', inputs=['x'], outputs=['y'], kernel_shape=[6, 6], strides=[2, 2], pads=[0, 0, 1, 1], auto_pad='NOTSET') return ([node], [x], [y]) @onnx_test def maxpool_same_upper_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 5, 5]) node = onnx.helper.make_node('MaxPool', inputs=['x'], outputs=['y'], kernel_shape=[2, 2], auto_pad='SAME_UPPER') return ([node], [x], [y]) @onnx_test def mean_broadcast_test(): data_0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 4]) data_1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 2, 3, 4]) data_2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [4]) data_3 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1]) data_4 = helper.make_tensor_value_info('4', TensorProto.FLOAT, [2, 3, 1]) mean = helper.make_tensor_value_info('mean', TensorProto.FLOAT, [1, 2, 3, 4]) node = onnx.helper.make_node("Mean", inputs=["0", "1", "2", "3", "4"], outputs=["mean"]) return ([node], [data_0, data_1, data_2, data_3, data_4], [mean]) @onnx_test def mean_fp16_test(): data_0 = helper.make_tensor_value_info('0', TensorProto.FLOAT16, [1, 2, 3]) data_1 = helper.make_tensor_value_info('1', TensorProto.FLOAT16, [1, 2, 3]) data_2 = helper.make_tensor_value_info('2', TensorProto.FLOAT16, [1, 2, 3]) mean = helper.make_tensor_value_info('mean', TensorProto.FLOAT16, [1, 2, 3]) node = onnx.helper.make_node("Mean", inputs=["0", "1", "2"], outputs=["mean"]) return ([node], [data_0, data_1, data_2], [mean]) @onnx_test def mean_invalid_broadcast_test(): data_0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 2, 3]) data_1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 2, 3]) data_2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 2, 4]) mean = helper.make_tensor_value_info('mean', TensorProto.FLOAT, [1, 2, 3]) node = onnx.helper.make_node("Mean", inputs=["0", "1", "2"], outputs=["mean"]) return ([node], [data_0, data_1, data_2], [mean]) @onnx_test def mean_single_input_test(): data_0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 2, 3]) mean = helper.make_tensor_value_info('mean', TensorProto.FLOAT, [1, 2, 3]) node = onnx.helper.make_node("Mean", inputs=["0"], outputs=["mean"]) return ([node], [data_0], [mean]) @onnx_test def mean_test(): data = [ helper.make_tensor_value_info(str(i), TensorProto.DOUBLE, [2, 2, 2]) for i in range(10) ] data_names = [str(i) for i in range(10)] mean = helper.make_tensor_value_info('mean', TensorProto.DOUBLE, [2, 2, 2]) node = onnx.helper.make_node("Mean", inputs=data_names, outputs=["mean"]) return ([node], data, [mean]) @onnx_test def min_test(): a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3]) b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3]) c = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3]) y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3]) node = onnx.helper.make_node( 'Min', inputs=['0', '1', '2'], outputs=['3'], ) return ([node], [a, b, c], [y]) @onnx_test def multinomial_test(): sample_size = 10 seed = 0.0 input = helper.make_tensor_value_info("input", TensorProto.FLOAT, [1, 10]) output = helper.make_tensor_value_info("output", TensorProto.INT32, [1, 10]) node = onnx.helper.make_node('Multinomial', inputs=['input'], sample_size=sample_size, seed=seed, outputs=['output']) return ([node], [input], [output]) @onnx_test def multinomial_generated_seed_test(): sample_size = 10 input = helper.make_tensor_value_info("input", TensorProto.FLOAT, [1, 10]) output = helper.make_tensor_value_info("output", TensorProto.INT32, [1, 10]) node = onnx.helper.make_node('Multinomial', inputs=['input'], sample_size=sample_size, outputs=['output']) return ([node], [input], [output]) @onnx_test def multinomial_dtype_error_test(): sample_size = 10 dtype = 0 input = helper.make_tensor_value_info("input", TensorProto.FLOAT, [1, 10]) output = helper.make_tensor_value_info("output", TensorProto.INT64, [1, 10]) node = onnx.helper.make_node('Multinomial', inputs=['input'], sample_size=sample_size, dtype=dtype, outputs=['output']) return ([node], [input], [output]) @onnx_test def multinomial_int64_test(): sample_size = 10 dtype = 7 seed = 1.0 input = helper.make_tensor_value_info("input", TensorProto.FLOAT, [1, 10]) output = helper.make_tensor_value_info("output", TensorProto.INT64, [1, 10]) node = onnx.helper.make_node('Multinomial', inputs=['input'], sample_size=sample_size, dtype=dtype, seed=seed, outputs=['output']) return ([node], [input], [output]) @onnx_test def neg_test(): x = helper.make_tensor_value_info('0', TensorProto.INT64, [2, 3]) y = helper.make_tensor_value_info('1', TensorProto.INT64, [2, 3]) node = onnx.helper.make_node('Neg', inputs=['0'], outputs=['1']) return ([node], [x], [y]) @onnx_test def nms_test(): b = helper.make_tensor_value_info('boxes', TensorProto.FLOAT, [1, 6, 4]) s = helper.make_tensor_value_info('scores', TensorProto.FLOAT, [1, 1, 6]) mo = helper.make_tensor_value_info('max_output_boxes_per_class', TensorProto.INT64, [1]) iou = helper.make_tensor_value_info('iou_threshold', TensorProto.FLOAT, [1]) st = helper.make_tensor_value_info('score_threshold', TensorProto.FLOAT, [1]) out = helper.make_tensor_value_info('selected_indices', TensorProto.INT64, [6, 3]) node = onnx.helper.make_node('NonMaxSuppression', inputs=[ 'boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold' ], outputs=['selected_indices'], center_point_box=1) return ([node], [b, s, mo, iou, st], [out]) @onnx_test def not_test(): x = helper.make_tensor_value_info('0', TensorProto.INT32, [4]) y = helper.make_tensor_value_info('1', TensorProto.INT32, [4]) node = onnx.helper.make_node('Not', inputs=['0'], outputs=['1']) return ([node], [x], [y]) @onnx_test def not_bool_test(): x = helper.make_tensor_value_info('0', TensorProto.BOOL, [4]) y = helper.make_tensor_value_info('1', TensorProto.BOOL, [4]) node = onnx.helper.make_node('Not', inputs=['0'], outputs=['1']) return ([node], [x], [y]) @onnx_test def no_pad_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 2]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2, 2]) node = onnx.helper.make_node('Pad', inputs=['0'], pads=[0, 0, 0, 0], outputs=['1']) return ([node], [x], [y]) @onnx_test def nonzero_dynamic_test(): x = helper.make_tensor_value_info('data', TensorProto.BOOL, [2, 2]) y = helper.make_tensor_value_info('indices', TensorProto.INT64, [2, 3]) node = onnx.helper.make_node('NonZero', inputs=['data'], outputs=['indices']) return ([node], [x], [y]) @onnx_test def nonzero_test(): data1 = np.array([[1., 0.], [1., 1.]]) data = helper.make_tensor(name='data', data_type=TensorProto.FLOAT, dims=data1.shape, vals=data1.flatten().astype(np.float)) y = helper.make_tensor_value_info('indices', TensorProto.INT64, [2, 3]) node = onnx.helper.make_node('NonZero', inputs=['data'], outputs=['indices']) return ([node], [], [y], [data]) @onnx_test def nonzero_int_test(): data1 = np.array([[1, 1, 0], [1, 0, 1]]) data = helper.make_tensor(name='data', data_type=TensorProto.INT16, dims=data1.shape, vals=data1.flatten().astype(np.int16)) y = helper.make_tensor_value_info('indices', TensorProto.INT64, [2, 4]) node = onnx.helper.make_node('NonZero', inputs=['data'], outputs=['indices']) return ([node], [], [y], [data]) @onnx_test def onehot_test(): axis_value = 0 depth = np.array([3]) indices = helper.make_tensor_value_info("indices", TensorProto.INT32, [5, 2]) values = helper.make_tensor_value_info("values", TensorProto.FLOAT16, [2]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT16, [3, 5, 2]) depth_tensor = helper.make_tensor(name="depth", data_type=TensorProto.INT32, dims=None, vals=depth.astype(int)) node = onnx.helper.make_node('OneHot', inputs=['indices', 'depth', 'values'], outputs=['y'], axis=axis_value) return ([node], [indices, values], [y], [depth_tensor]) @onnx_test def pad_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 2]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 4]) node = onnx.helper.make_node('Pad', inputs=['0'], pads=[1, 1, 1, 1], outputs=['1']) return ([node], [x], [y]) @onnx_test def pad_3arg_test(): values = np.array([1]) val_tensor = helper.make_tensor(name='val', data_type=TensorProto.FLOAT, dims=values.reshape(()).shape, vals=values.astype(float)) arg_val = onnx.helper.make_node('Constant', inputs=[], outputs=['arg_val'], value=val_tensor) sizes = np.array([1, 1, 2, 2]) pad_tensor = helper.make_tensor(name='pad_size', data_type=TensorProto.INT32, dims=sizes.shape, vals=sizes.astype(int)) arg_pad = onnx.helper.make_node('Constant', inputs=[], outputs=['arg_pad'], value=pad_tensor) x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 2]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [5, 5]) node = onnx.helper.make_node('Pad', inputs=['0', 'arg_pad', 'arg_val'], outputs=['1']) return ([arg_val, arg_pad, node], [x], [y]) @onnx_test def pad_reflect_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 2]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2, 5]) sizes = np.array([0, 2, 0, 1]) pad_tensor = helper.make_tensor(name='pad_size', data_type=TensorProto.INT32, dims=sizes.shape, vals=sizes.astype(int)) arg_pad = onnx.helper.make_node('Constant', inputs=[], outputs=['arg_pad'], value=pad_tensor) node = onnx.helper.make_node('Pad', mode='reflect', inputs=['0', 'arg_pad'], outputs=['1']) return ([arg_pad, node], [x], [y]) @onnx_test def pad_reflect_multiaxis_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 5]) sizes = np.array([0, 2, 2, 0]) pad_tensor = helper.make_tensor(name='pad_size', data_type=TensorProto.INT32, dims=sizes.shape, vals=sizes.astype(int)) arg_pad = onnx.helper.make_node('Constant', inputs=[], outputs=['arg_pad'], value=pad_tensor) node = onnx.helper.make_node('Pad', mode='reflect', inputs=['0', 'arg_pad'], outputs=['1']) return ([arg_pad, node], [x], [y]) @onnx_test def pow_test(): arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5]) arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2, 3, 4, 5]) arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT, [2, 3, 4, 5]) node = onnx.helper.make_node( 'Pow', inputs=['0', '1'], outputs=['out'], ) return ([node], [arg0, arg1], [arg_out]) @onnx_test def pow_fp32_i64_test(): arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5]) arg1 = helper.make_tensor_value_info('1', TensorProto.INT64, [2, 3, 4, 5]) arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT, [2, 3, 4, 5]) node = onnx.helper.make_node( 'Pow', inputs=['0', '1'], outputs=['out'], ) return ([node], [arg0, arg1], [arg_out]) @onnx_test def pow_i64_fp32_test(): arg0 = helper.make_tensor_value_info('0', TensorProto.INT64, [2, 3, 4, 5]) arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2, 3, 4, 5]) arg_out = helper.make_tensor_value_info('out', TensorProto.INT64, [2, 3, 4, 5]) node = onnx.helper.make_node( 'Pow', inputs=['0', '1'], outputs=['out'], ) return ([node], [arg0, arg1], [arg_out]) @onnx_test def prefix_scan_sum_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 2, 2]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 2, 2]) axis_val = np.array([0]) axis_tensor = helper.make_tensor(name="axis", data_type=TensorProto.INT32, dims=axis_val.shape, vals=axis_val.astype(int)) node = onnx.helper.make_node('CumSum', inputs=['x', 'axis'], outputs=['y'], exclusive=1, reverse=1) return ([node], [x], [y], [axis_tensor]) @onnx_test def prelu_brcst_test(): arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5]) arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 5]) arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT, [2, 3, 4, 5]) node = onnx.helper.make_node( 'PRelu', inputs=['0', '1'], outputs=['out'], ) return ([node], [arg0, arg1], [arg_out]) @onnx_test def quantizelinear_test(): arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [5]) arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1]) arg_out = helper.make_tensor_value_info('out', TensorProto.INT8, [5]) node = onnx.helper.make_node( 'QuantizeLinear', inputs=['0', '1'], outputs=['out'], ) return ([node], [arg0, arg1], [arg_out]) @onnx_test def quantizelinear_int32_test(): arg0 = helper.make_tensor_value_info('0', TensorProto.INT32, [5]) arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1]) arg_out = helper.make_tensor_value_info('out', TensorProto.INT8, [5]) node = onnx.helper.make_node( 'QuantizeLinear', inputs=['0', '1'], outputs=['out'], ) return ([node], [arg0, arg1], [arg_out]) @onnx_test def quantizelinear_zero_point_test(): arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [5]) arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1]) arg2 = helper.make_tensor_value_info('2', TensorProto.INT8, [1]) arg_out = helper.make_tensor_value_info('out', TensorProto.INT8, [5]) node = onnx.helper.make_node( 'QuantizeLinear', inputs=['0', '1', '2'], outputs=['out'], ) return ([node], [arg0, arg1, arg2], [arg_out]) def make_quantizelinear_axis_graph(axis): arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 1, 5, 1]) arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [5]) arg2 = helper.make_tensor_value_info('2', TensorProto.INT8, [5]) arg_out = helper.make_tensor_value_info('out', TensorProto.INT8, [1, 1, 5, 1]) node = onnx.helper.make_node('QuantizeLinear', inputs=['0', '1', '2'], outputs=['out'], axis=axis) return ([node], [arg0, arg1, arg2], [arg_out]) @onnx_test def quantizelinear_axis_test(): return make_quantizelinear_axis_graph(2) @onnx_test def quantizelinear_neg_axis_test(): return make_quantizelinear_axis_graph(-2) @onnx_test def randomnormal_test(): dtype = 11 mean = 10.0 scale = 1.5 seed = 0.0 shape = [2, 3, 4] output = helper.make_tensor_value_info('output', TensorProto.DOUBLE, [2, 3, 4]) node = onnx.helper.make_node('RandomNormal', inputs=[], outputs=['output'], dtype=dtype, mean=mean, scale=scale, seed=seed, shape=shape) return ([node], [], [output]) @onnx_test def randomnormal_dtype_error_test(): dtype = 6 shape = [2, 3, 4] output = helper.make_tensor_value_info('output', TensorProto.INT32, [2, 3, 4]) node = onnx.helper.make_node('RandomNormal', inputs=[], outputs=['output'], dtype=dtype, shape=shape) return ([node], [], [output]) @onnx_test def randomnormal_generated_seed_test(): sample_size = 10 input = helper.make_tensor_value_info("input", TensorProto.FLOAT, [1, 10]) output = helper.make_tensor_value_info("output", TensorProto.INT32, [1, 10]) node = onnx.helper.make_node('RandomNormal', inputs=['input'], sample_size=sample_size, outputs=['output']) return ([node], [input], [output]) @onnx_test def randomnormal_shape_error_test(): dtype = 1 output = helper.make_tensor_value_info('output', TensorProto.FLOAT, [2, 3, 4]) node = onnx.helper.make_node('RandomNormal', inputs=[], outputs=['output'], dtype=dtype) return ([node], [], [output]) @onnx_test def randomnormallike_test(): dtype = 10 mean = 10.0 scale = 1.5 seed = 0.0 input = helper.make_tensor_value_info('input', TensorProto.FLOAT16, [2, 3, 4]) output = helper.make_tensor_value_info('output', TensorProto.FLOAT16, [2, 3, 4]) node = onnx.helper.make_node('RandomNormalLike', inputs=['input'], outputs=['output'], dtype=dtype, mean=mean, scale=scale, seed=seed) return ([node], [input], [output]) @onnx_test def randomnormallike_type_error_test(): seed = 0 input = helper.make_tensor_value_info('input', TensorProto.INT32, [2, 3, 4]) output = helper.make_tensor_value_info('output', TensorProto.FLOAT, [2, 3, 4]) node = onnx.helper.make_node('RandomNormalLike', inputs=['input'], outputs=['output'], seed=seed) return ([node], [input], [output]) @onnx_test def randomuniform_test(): dtype = 11 high = 1.0 low = 0.0 seed = 0.0 shape = [2, 3, 4] output = helper.make_tensor_value_info('output', TensorProto.DOUBLE, [2, 3, 4]) node = onnx.helper.make_node('RandomUniform', inputs=[], outputs=['output'], dtype=dtype, high=high, low=low, seed=seed, shape=shape) return ([node], [], [output]) @onnx_test def randomuniform_dtype_error_test(): dtype = 6 shape = [2, 3, 4] output = helper.make_tensor_value_info('output', TensorProto.INT32, [2, 3, 4]) node = onnx.helper.make_node('RandomUniform', inputs=[], outputs=['output'], dtype=dtype, shape=shape) return ([node], [], [output]) @onnx_test def randomuniform_generated_seed_test(): sample_size = 10 input = helper.make_tensor_value_info("input", TensorProto.FLOAT, [1, 10]) output = helper.make_tensor_value_info("output", TensorProto.INT32, [1, 10]) node = onnx.helper.make_node('RandomUniform', inputs=['input'], sample_size=sample_size, outputs=['output']) return ([node], [input], [output]) @onnx_test def randomuniform_shape_error_test(): dtype = 1 output = helper.make_tensor_value_info('output', TensorProto.FLOAT, [2, 3, 4]) node = onnx.helper.make_node('RandomUniform', inputs=[], outputs=['output'], dtype=dtype) return ([node], [], [output]) @onnx_test def randomuniformlike_test(): dtype = 10 high = 10.0 low = 1.0 seed = 0.0 input = helper.make_tensor_value_info('input', TensorProto.FLOAT16, [2, 3, 4]) output = helper.make_tensor_value_info('output', TensorProto.FLOAT16, [2, 3, 4]) node = onnx.helper.make_node('RandomUniformLike', inputs=['input'], outputs=['output'], dtype=dtype, high=high, low=low, seed=seed) return ([node], [input], [output]) @onnx_test def randomuniformlike_type_error_test(): seed = 0 input = helper.make_tensor_value_info('input', TensorProto.INT32, [2, 3, 4]) output = helper.make_tensor_value_info('output', TensorProto.FLOAT, [2, 3, 4]) node = onnx.helper.make_node('RandomUniformLike', inputs=['input'], outputs=['output'], seed=seed) return ([node], [input], [output]) @onnx_test def range_test(): start_val = np.array([10]) limit_val = np.array([6]) delta_val = np.array([-3]) start_tensor = helper.make_tensor(name='start_val', data_type=TensorProto.INT64, dims=start_val.reshape(()).shape, vals=start_val.astype(np.int64)) start = onnx.helper.make_node('Constant', inputs=[], outputs=['start'], value=start_tensor) limit_tensor = helper.make_tensor(name='limit_val', data_type=TensorProto.INT64, dims=limit_val.reshape(()).shape, vals=limit_val.astype(np.int64)) limit = onnx.helper.make_node('Constant', inputs=[], outputs=['limit'], value=limit_tensor) delta_tensor = helper.make_tensor(name='delta_val', data_type=TensorProto.INT64, dims=delta_val.reshape(()).shape, vals=delta_val.astype(np.int64)) delta = onnx.helper.make_node('Constant', inputs=[], outputs=['delta'], value=delta_tensor) node = onnx.helper.make_node('Range', inputs=['start', 'limit', 'delta'], outputs=['1']) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3]) return ([start, limit, delta, node], [], [y]) @onnx_test def range_float_test(): start_val = np.array([2]) limit_val = np.array([11]) delta_val = np.array([2]) start_tensor = helper.make_tensor(name='start_val', data_type=TensorProto.FLOAT, dims=start_val.reshape(()).shape, vals=start_val.astype(np.float)) start = onnx.helper.make_node('Constant', inputs=[], outputs=['start'], value=start_tensor) limit_tensor = helper.make_tensor(name='limit_val', data_type=TensorProto.FLOAT, dims=limit_val.reshape(()).shape, vals=limit_val.astype(np.float)) limit = onnx.helper.make_node('Constant', inputs=[], outputs=['limit'], value=limit_tensor) delta_tensor = helper.make_tensor(name='delta_val', data_type=TensorProto.FLOAT, dims=delta_val.reshape(()).shape, vals=delta_val.astype(np.float)) delta = onnx.helper.make_node('Constant', inputs=[], outputs=['delta'], value=delta_tensor) node = onnx.helper.make_node('Range', inputs=['start', 'limit', 'delta'], outputs=['1']) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3]) return ([start, limit, delta, node], [], [y]) @onnx_test def recip_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3]) node = onnx.helper.make_node( 'Reciprocal', inputs=['x'], outputs=['y'], ) return ([node], [x], [y]) @onnx_test def reducel1_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 6]) axes = [-2] node = onnx.helper.make_node('ReduceL1', inputs=['x'], outputs=['y'], axes=axes, keepdims=0) return ([node], [x], [y]) @onnx_test def reducel2_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 5]) axes = [-1] node = onnx.helper.make_node('ReduceL2', inputs=['x'], outputs=['y'], axes=axes, keepdims=0) return ([node], [x], [y]) @onnx_test def reduce_log_sum_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 1, 5, 6]) axes = [-3] node = onnx.helper.make_node('ReduceLogSum', inputs=['x'], outputs=['y'], axes=axes, keepdims=1) return ([node], [x], [y]) @onnx_test def reduce_log_sum_exp_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [4, 5, 6]) axes = [-4] node = onnx.helper.make_node('ReduceLogSumExp', inputs=['x'], outputs=['y'], axes=axes, keepdims=1) return ([node], [x], [y]) @onnx_test def reducemax_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 6]) axes = [2] node = onnx.helper.make_node('ReduceMax', inputs=['x'], outputs=['y'], axes=axes, keepdims=0) return ([node], [x], [y]) @onnx_test def reducemean_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4]) axes = [2, 3] node = onnx.helper.make_node('ReduceMean', inputs=['x'], outputs=['y'], axes=axes, keepdims=0) return ([node], [x], [y]) @onnx_test def reducemean_keepdims_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 6]) axes = [2] node = onnx.helper.make_node('ReduceMean', inputs=['x'], outputs=['y'], axes=axes, keepdims=1) return ([node], [x], [y]) @onnx_test def reducemin_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 1, 5, 1]) axes = [1, 3] node = onnx.helper.make_node('ReduceMin', inputs=['x'], outputs=['y'], axes=axes, keepdims=1) return ([node], [x], [y]) @onnx_test def reduceprod_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 6]) axes = [2] node = onnx.helper.make_node('ReduceProd', inputs=['x'], outputs=['y'], axes=axes, keepdims=1) return ([node], [x], [y]) @onnx_test def reducesum_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 6]) axes = [2] node = onnx.helper.make_node('ReduceSum', inputs=['x'], outputs=['y'], axes=axes, keepdims=0) return ([node], [x], [y]) @onnx_test def reducesum_empty_axes_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 6]) axes = np.array([], dtype=np.int64) axes_tensor = helper.make_tensor(name="axes", data_type=TensorProto.INT64, dims=axes.shape, vals=axes.astype(np.int64)) node = onnx.helper.make_node('ReduceSum', inputs=['x', 'axes'], outputs=['y'], keepdims=0, noop_with_empty_axes=False) return ([node], [x], [y], [axes_tensor]) @onnx_test def reducesum_noop_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 6]) axes = np.array([], dtype=np.int64) axes_tensor = helper.make_tensor(name="axes", data_type=TensorProto.INT64, dims=axes.shape, vals=axes.astype(np.int64)) node = onnx.helper.make_node('ReduceSum', inputs=['x', 'axes'], outputs=['y'], keepdims=0, noop_with_empty_axes=True) return ([node], [x], [y], [axes_tensor]) @onnx_test def reducesum_keepdims_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 1]) axes = [2, 3] node = onnx.helper.make_node('ReduceSum', inputs=['x'], outputs=['y'], axes=axes, keepdims=1) return ([node], [x], [y]) @onnx_test def reducesum_multiaxis_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 1]) axes = [2, 3] node = onnx.helper.make_node('ReduceSum', inputs=['x'], outputs=['y'], axes=axes, keepdims=0) return ([node], [x], [y]) @onnx_test def reducesum_square_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 6]) axes = [-2] node = onnx.helper.make_node('ReduceSumSquare', inputs=['x'], outputs=['y'], axes=axes, keepdims=0) return ([node], [x], [y]) @onnx_test def reshape_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [4, 2, 3]) x_shape = helper.make_tensor_value_info('1', TensorProto.INT64, [2]) x_shape_list = [3, 8] y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3, 8]) y2 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [3, 8]) node = onnx.helper.make_node('Reshape', inputs=['0', '1'], outputs=['2']) node2 = onnx.helper.make_node('Reshape', inputs=['0'], shape=x_shape_list, outputs=['3']) return ([node, node2], [x, x_shape], [y, y2], [helper.make_tensor('1', TensorProto.INT64, [2], [3, 8])]) @onnx_test def reshape_non_standard_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 3, 4]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [4, 3, 2]) trans = helper.make_node( 'Transpose', inputs=['x'], outputs=['trans_x'], perm=[0, 2, 1], ) res = onnx.helper.make_node('Reshape', inputs=['trans_x'], outputs=['y'], shape=[4, 3, 2]) return ([trans, res], [x], [y]) @onnx_test def resize_downsample_f_test(): scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) scale_tensor = helper.make_tensor(name='scales', data_type=TensorProto.FLOAT, dims=scales.shape, vals=scales.flatten().astype(np.float32)) X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [1, 1, 2, 4]) Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, []) node = onnx.helper.make_node( 'Resize', inputs=['X', '', 'scales'], outputs=['Y'], coordinate_transformation_mode='align_corners', mode='nearest', nearest_mode='floor') return ([node], [X], [Y], [scale_tensor]) @onnx_test def resize_downsample_c_test(): scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) scale_tensor = helper.make_tensor(name='scales', data_type=TensorProto.FLOAT, dims=scales.shape, vals=scales.flatten().astype(np.float32)) X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [1, 1, 2, 4]) Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [1, 1, 1, 2]) node = onnx.helper.make_node('Resize', inputs=['X', '', 'scales'], outputs=['Y'], coordinate_transformation_mode='asymmetric', mode='nearest', nearest_mode='ceil') return ([node], [X], [Y], [scale_tensor]) @onnx_test def resize_downsample_linear_test(): scales = np.array([1.0, 1.0, 0.6, 0.5], dtype=np.float32) scale_tensor = helper.make_tensor(name='scales', data_type=TensorProto.FLOAT, dims=scales.shape, vals=scales.flatten().astype(np.float32)) X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [1, 1, 2, 4]) Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, []) node = onnx.helper.make_node('Resize', inputs=['X', '', 'scales'], outputs=['Y'], mode='linear') return ([node], [X], [Y], [scale_tensor]) @onnx_test def resize_nonstd_input_test(): scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) scale_tensor = helper.make_tensor(name='scales', data_type=TensorProto.FLOAT, dims=scales.shape, vals=scales.flatten().astype(np.float32)) X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [1, 1, 4, 2]) Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [1, 1, 1, 2]) trn = onnx.helper.make_node('Transpose', inputs=['X'], outputs=['TX'], perm=[0, 1, 3, 2]) node = onnx.helper.make_node('Resize', inputs=['TX', '', 'scales'], outputs=['Y'], coordinate_transformation_mode='asymmetric', mode='nearest', nearest_mode='ceil') return ([trn, node], [X], [Y], [scale_tensor]) @onnx_test def resize_outsize_test(): out_lens = np.array([1, 1, 4, 6], dtype=np.int64) out_lens_tensor = helper.make_tensor(name='out_lens', data_type=TensorProto.INT64, dims=out_lens.shape, vals=out_lens.flatten().astype( np.int64)) X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [1, 1, 2, 2]) Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [1, 1, 4, 6]) node = onnx.helper.make_node( 'Resize', inputs=['X', '', '', 'out_lens'], outputs=['Y'], coordinate_transformation_mode='tf_half_pixel_for_nn', mode='nearest', nearest_mode='round_prefer_floor') return ([node], [X], [Y], [out_lens_tensor]) @onnx_test def resize_upsample_linear_ac_test(): scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) scales_tensor = helper.make_tensor(name='scales', data_type=TensorProto.FLOAT, dims=scales.shape, vals=scales.flatten().astype( np.float32)) X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [1, 1, 2, 2]) Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, []) node = onnx.helper.make_node( 'Resize', inputs=['X', '', 'scales'], outputs=['Y'], mode='linear', coordinate_transformation_mode='align_corners') return ([node], [X], [Y], [scales_tensor]) @onnx_test def resize_upsample_linear_test(): scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) scales_tensor = helper.make_tensor(name='scales', data_type=TensorProto.FLOAT, dims=scales.shape, vals=scales.flatten().astype( np.float32)) X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [1, 1, 2, 2]) Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, []) node = onnx.helper.make_node('Resize', inputs=['X', '', 'scales'], outputs=['Y'], mode='linear') return ([node], [X], [Y], [scales_tensor]) @onnx_test def resize_upsample_pf_test(): scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32) scale_tensor = helper.make_tensor(name='scales', data_type=TensorProto.FLOAT, dims=scales.shape, vals=scales.flatten().astype(np.float32)) X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [1, 1, 2, 2]) Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [1, 1, 4, 6]) node = onnx.helper.make_node('Resize', inputs=['X', '', 'scales'], outputs=['Y'], mode='nearest') return ([node], [X], [Y], [scale_tensor]) @onnx_test def resize_upsample_pc_test(): scales = np.array([1.0, 1.0, 2.0, 1.5], dtype=np.float32) scale_tensor = helper.make_tensor(name='scales', data_type=TensorProto.FLOAT, dims=scales.shape, vals=scales.flatten().astype(np.float32)) X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [1, 1, 2, 4]) Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [1, 1, 4, 6]) node = onnx.helper.make_node( 'Resize', inputs=['X', '', 'scales'], outputs=['Y'], coordinate_transformation_mode='pytorch_half_pixel', mode='nearest', exclude_outside=0, nearest_mode='round_prefer_ceil') return ([node], [X], [Y], [scale_tensor]) @onnx_test def roialign_default_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 4, 7, 8]) roi = helper.make_tensor_value_info('rois', TensorProto.FLOAT, [8, 4]) bi = helper.make_tensor_value_info('batch_ind', TensorProto.INT64, [8]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [8, 4, 1, 1]) node = onnx.helper.make_node('RoiAlign', inputs=['x', 'rois', 'batch_ind'], outputs=['y']) return ([node], [x, roi, bi], [y]) @onnx_test def roialign_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 5, 4, 7]) roi = helper.make_tensor_value_info('rois', TensorProto.FLOAT, [8, 4]) bi = helper.make_tensor_value_info('batch_ind', TensorProto.INT64, [8]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [8, 4, 5, 5]) node = onnx.helper.make_node( 'RoiAlign', inputs=['x', 'rois', 'batch_ind'], outputs=['y'], spatial_scale=2.0, output_height=5, output_width=5, sampling_ratio=3, mode="avg", coordinate_transformation_mode="output_half_pixel") return ([node], [x, roi, bi], [y]) @onnx_test def scatter_test(): x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 4, 5, 6]) i = helper.make_tensor_value_info('indices', TensorProto.INT32, [2, 3, 4, 5]) u = helper.make_tensor_value_info('update', TensorProto.FLOAT, [2, 3, 4, 5]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 5, 6]) node = onnx.helper.make_node( 'Scatter', inputs=['data', 'indices', 'update'], outputs=['y'], axis=-2, ) return ([node], [x, i, u], [y]) @onnx_test def selu_test(): x = helper.make_tensor_value_info('x', TensorProto.DOUBLE, [2, 3]) y = helper.make_tensor_value_info('y', TensorProto.DOUBLE, [2, 3]) node = onnx.helper.make_node('Selu', inputs=['x'], outputs=['y'], alpha=0.3, gamma=0.5) return ([node], [x], [y]) @onnx_test def shape_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6]) y = helper.make_tensor_value_info('y', TensorProto.INT64, [4]) node = onnx.helper.make_node( 'Shape', inputs=['x'], outputs=['y'], ) return ([node], [x], [y]) @onnx_test def shape_gather_test(): values = np.array([1]) # value = helper.make_tensor_value_info('value', TensorProto.INT32, [1]) x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [7, 3, 10]) z = helper.make_tensor_value_info('z', TensorProto.FLOAT, [1]) value_tensor = helper.make_tensor(name='const_tensor', data_type=TensorProto.INT32, dims=values.shape, vals=values.flatten().astype(int)) node_const = onnx.helper.make_node( 'Constant', inputs=[], outputs=['value'], value=value_tensor, ) node_shape = onnx.helper.make_node( 'Shape', inputs=['x'], outputs=['y'], ) node_gather = helper.make_node( 'Gather', inputs=['y', 'value'], outputs=['z'], axis=0, ) return ([node_const, node_shape, node_gather], [x], [z]) @onnx_test def sign_test(): x = helper.make_tensor_value_info('x', TensorProto.DOUBLE, [10, 5]) y = helper.make_tensor_value_info('y', TensorProto.DOUBLE, [10, 5]) node = onnx.helper.make_node( 'Sign', inputs=['x'], outputs=['y'], ) return ([node], [x], [y]) @onnx_test def sin_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10]) node = onnx.helper.make_node( 'Sin', inputs=['x'], outputs=['y'], ) return ([node], [x], [y]) @onnx_test def sinh_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10]) node = onnx.helper.make_node( 'Sinh', inputs=['x'], outputs=['y'], ) return ([node], [x], [y]) @onnx_test def slice_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3, 2]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 2]) node = onnx.helper.make_node('Slice', inputs=['0'], axes=[0, 1], starts=[1, 0], ends=[2, 2], outputs=['1']) return ([node], [x], [y]) @onnx_test def slice_3arg_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [5, 5]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2, 5]) start = np.array([0, 0]) start_tensor = helper.make_tensor(name="start", data_type=TensorProto.INT32, dims=start.shape, vals=start.astype(int)) arg_start = helper.make_node("Constant", inputs=[], outputs=['arg_start'], value=start_tensor) end = np.array([2, 5]) end_tensor = helper.make_tensor(name="end", data_type=TensorProto.INT32, dims=end.shape, vals=end.astype(int)) arg_end = helper.make_node("Constant", inputs=[], outputs=['arg_end'], value=end_tensor) node = onnx.helper.make_node('Slice', inputs=['0', 'arg_start', 'arg_end'], outputs=['1']) return ([arg_start, arg_end, node], [x], [y]) @onnx_test def slice_5arg_test(): step = np.array([1, 1]) step_tensor = helper.make_tensor(name="step", data_type=TensorProto.INT32, dims=step.shape, vals=step.astype(int)) arg_step = helper.make_node("Constant", inputs=[], outputs=['arg_step'], value=step_tensor) axis = np.array([-1, -2]) axis_tensor = helper.make_tensor(name="axis", data_type=TensorProto.INT32, dims=axis.shape, vals=axis.astype(int)) arg_axis = helper.make_node("Constant", inputs=[], outputs=['arg_axis'], value=axis_tensor) end = np.array([-1, -1]) end_tensor = helper.make_tensor(name="end", data_type=TensorProto.INT32, dims=end.shape, vals=end.astype(int)) arg_end = helper.make_node("Constant", inputs=[], outputs=['arg_end'], value=end_tensor) start = np.array([-5, -3]) start_tensor = helper.make_tensor(name="start", data_type=TensorProto.INT32, dims=start.shape, vals=start.astype(int)) arg_start = helper.make_node("Constant", inputs=[], outputs=['arg_start'], value=start_tensor) x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [5, 5]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 2]) node = onnx.helper.make_node( 'Slice', inputs=['0', 'arg_start', 'arg_end', 'arg_axis', 'arg_step'], outputs=['1']) return ([arg_step, arg_axis, arg_end, arg_start, node], [x], [y]) @onnx_test def slice_5arg_reverse_test(): step = np.array([-1, 1]) step_tensor = helper.make_tensor(name="step", data_type=TensorProto.INT32, dims=step.shape, vals=step.astype(int)) arg_step = helper.make_node("Constant", inputs=[], outputs=['arg_step'], value=step_tensor) axis = np.array([-1, -2]) axis_tensor = helper.make_tensor(name="axis", data_type=TensorProto.INT32, dims=axis.shape, vals=axis.astype(int)) arg_axis = helper.make_node("Constant", inputs=[], outputs=['arg_axis'], value=axis_tensor) end = np.array([-5, -1]) end_tensor = helper.make_tensor(name="end", data_type=TensorProto.INT32, dims=end.shape, vals=end.astype(int)) arg_end = helper.make_node("Constant", inputs=[], outputs=['arg_end'], value=end_tensor) start = np.array([-1, -3]) start_tensor = helper.make_tensor(name="start", data_type=TensorProto.INT32, dims=start.shape, vals=start.astype(int)) arg_start = helper.make_node("Constant", inputs=[], outputs=['arg_start'], value=start_tensor) x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [5, 5]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 2]) node = onnx.helper.make_node( 'Slice', inputs=['0', 'arg_start', 'arg_end', 'arg_axis', 'arg_step'], outputs=['1']) return ([arg_step, arg_axis, arg_end, arg_start, node], [x], [y]) @onnx_test def slice_5arg_step_test(): step = np.array([-2, 2]) step_tensor = helper.make_tensor(name="step", data_type=TensorProto.INT32, dims=step.shape, vals=step.astype(int)) arg_step = helper.make_node("Constant", inputs=[], outputs=['arg_step'], value=step_tensor) axis = np.array([-1, -2]) axis_tensor = helper.make_tensor(name="axis", data_type=TensorProto.INT32, dims=axis.shape, vals=axis.astype(int)) arg_axis = helper.make_node("Constant", inputs=[], outputs=['arg_axis'], value=axis_tensor) end = np.array([-5, -1]) end_tensor = helper.make_tensor(name="end", data_type=TensorProto.INT32, dims=end.shape, vals=end.astype(int)) arg_end = helper.make_node("Constant", inputs=[], outputs=['arg_end'], value=end_tensor) start = np.array([-1, -3]) start_tensor = helper.make_tensor(name="start", data_type=TensorProto.INT32, dims=start.shape, vals=start.astype(int)) arg_start = helper.make_node("Constant", inputs=[], outputs=['arg_start'], value=start_tensor) x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [5, 5]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 2]) node = onnx.helper.make_node( 'Slice', inputs=['0', 'arg_start', 'arg_end', 'arg_axis', 'arg_step'], outputs=['1']) return ([arg_step, arg_axis, arg_end, arg_start, node], [x], [y]) @onnx_test def slice_max_end_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [10, 20]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [9, 17]) node = onnx.helper.make_node('Slice', inputs=['0'], axes=[0, 1], starts=[1, 2], ends=[3000000000, -1], outputs=['1']) return ([node], [x], [y]) @onnx_test def softmax_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3]) node = onnx.helper.make_node('Softmax', inputs=['0'], outputs=['1']) return ([node], [x], [y]) @onnx_test def softmax_nonstd_input_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [6, 8]) y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3, 4]) node0 = onnx.helper.make_node('Slice', inputs=['0'], axes=[0, 1], starts=[1, 0], ends=[4, 4], outputs=['1']) node1 = onnx.helper.make_node('Softmax', inputs=['1'], outputs=['2']) return ([node0, node1], [x], [y]) @onnx_test def softsign_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [5]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [5]) node = onnx.helper.make_node('Softsign', inputs=['x'], outputs=['y']) return ([node], [x], [y]) def softplus_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [5]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [5]) node = onnx.helper.make_node('Softplus', inputs=['x'], outputs=['y']) return ([node], [x], [y]) @onnx_test def softsign_nd_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT16, [3, 4, 5]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT16, [3, 4, 5]) node = onnx.helper.make_node('Softsign', inputs=['x'], outputs=['y']) return ([node], [x], [y]) def softplus_nd_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT16, [3, 4, 5]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT16, [3, 4, 5]) node = onnx.helper.make_node('Softplus', inputs=['x'], outputs=['y']) return ([node], [x], [y]) @onnx_test def split_minus_axis_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15]) y1 = helper.make_tensor_value_info('y1', TensorProto.FLOAT, [10, 5]) y2 = helper.make_tensor_value_info('y2', TensorProto.FLOAT, [10, 5]) y3 = helper.make_tensor_value_info('y3', TensorProto.FLOAT, [10, 5]) node = onnx.helper.make_node( 'Split', inputs=['x'], outputs=['y1', 'y2', 'y3'], axis=-1, ) return ([node], [x], [y1, y2, y3]) @onnx_test def split_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15]) y1 = helper.make_tensor_value_info('y1', TensorProto.FLOAT, [10, 7]) y2 = helper.make_tensor_value_info('y2', TensorProto.FLOAT, [10, 4]) y3 = helper.make_tensor_value_info('y3', TensorProto.FLOAT, [10, 4]) node = onnx.helper.make_node('Split', inputs=['x'], outputs=['y1', 'y2', 'y3'], axis=1, split=[7, 4, 4]) return ([node], [x], [y1, y2, y3]) @onnx_test def split_test_default(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15]) y1 = helper.make_tensor_value_info('y1', TensorProto.FLOAT, [5, 15]) y2 = helper.make_tensor_value_info('y2', TensorProto.FLOAT, [5, 15]) node = onnx.helper.make_node( 'Split', inputs=['x'], outputs=['y1', 'y2'], ) return ([node], [x], [y1, y2]) @onnx_test def sqrt_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10, 15]) node = onnx.helper.make_node( 'Sqrt', inputs=['x'], outputs=['y'], ) return ([node], [x], [y]) @onnx_test def squeeze_axes_input_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 1, 5, 1]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 5]) axes = np.array([1, 3], dtype=np.int64) axes_tensor = helper.make_tensor(name="axes", data_type=TensorProto.INT64, dims=axes.shape, vals=axes.astype(np.int64)) node = onnx.helper.make_node('Squeeze', inputs=['x', 'axes'], outputs=['y']) return ([node], [x], [y], [axes_tensor]) @onnx_test def squeeze_empty_axes_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 1, 5, 1]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 5]) axes = np.array([], dtype=np.int64) axes_tensor = helper.make_tensor(name="axes", data_type=TensorProto.INT64, dims=axes.shape, vals=axes.astype(np.int64)) node = onnx.helper.make_node('Squeeze', inputs=['x', 'axes'], outputs=['y']) return ([node], [x], [y], [axes_tensor]) @onnx_test def squeeze_unsqueeze_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 1, 1, 2, 1]) y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 3, 1, 2, 1]) node = onnx.helper.make_node('Squeeze', inputs=['0'], axes=[0, 2, 3, 5], outputs=['1']) node2 = onnx.helper.make_node('Unsqueeze', inputs=['1'], axes=[0, 1, 3, 5], outputs=['2']) return ([node, node2], [x], [y]) @onnx_test def sub_bcast_test(): arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5]) arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4]) arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT, [2, 3, 4, 5]) node = onnx.helper.make_node( 'Sub', inputs=['0', '1'], outputs=['out'], broadcast=1, axis=1, ) return ([node], [arg0, arg1], [arg_out]) @onnx_test def sub_scalar_test(): values = np.array([1]) arg_node = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5]) arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT, [2, 3, 4, 5]) values_tensor = helper.make_tensor(name='const', data_type=TensorProto.FLOAT, dims=values.reshape(()).shape, vals=values.flatten().astype(float)) arg_const = onnx.helper.make_node( 'Constant', inputs=[], outputs=['arg_const'], value=values_tensor, ) node = onnx.helper.make_node( 'Sub', inputs=['0', 'arg_const'], outputs=['out'], ) return ([arg_const, node], [arg_node], [arg_out]) @onnx_test def sum_int_test(): a = helper.make_tensor_value_info('0', TensorProto.INT16, [3]) b = helper.make_tensor_value_info('1', TensorProto.UINT16, [3]) c = helper.make_tensor_value_info('2', TensorProto.UINT32, [3]) y = helper.make_tensor_value_info('3', TensorProto.UINT32, [3]) cnode1 = onnx.helper.make_node('Cast', inputs=['0'], outputs=['c0'], to=12) cnode2 = onnx.helper.make_node('Cast', inputs=['1'], outputs=['c1'], to=12) node = onnx.helper.make_node( 'Sum', inputs=['c0', 'c1', '2'], outputs=['3'], ) return ([cnode1, cnode2, node], [a, b, c], [y]) @onnx_test def sum_test(): a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3]) b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3]) c = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3]) y = helper.make_tensor_value_info('3', TensorProto.FLOAT, [3]) node = onnx.helper.make_node( 'Sum', inputs=['0', '1', '2'], outputs=['3'], ) return ([node], [a, b, c], [y]) @onnx_test def sum_type_test(): valb = np.array([1, 0]) t_bool = helper.make_tensor(name="bool", data_type=TensorProto.BOOL, dims=valb.shape, vals=valb.astype(np.bool)) val = np.array([1, 1]) t_int8 = helper.make_tensor(name="int8", data_type=TensorProto.INT8, dims=val.shape, vals=val.astype(np.int8)) t_uint8 = helper.make_tensor(name="uint8", data_type=TensorProto.UINT8, dims=val.shape, vals=val.astype(np.uint8)) t_uint16 = helper.make_tensor(name="uint16", data_type=TensorProto.UINT16, dims=val.shape, vals=val.astype(np.uint16)) t_uint32 = helper.make_tensor(name="uint32", data_type=TensorProto.UINT32, dims=val.shape, vals=val.astype(np.uint32)) t_uint64 = helper.make_tensor(name="uint64", data_type=TensorProto.UINT64, dims=val.shape, vals=val.astype(np.uint64)) t_double = helper.make_tensor(name="double", data_type=TensorProto.DOUBLE, dims=val.shape, vals=val.astype(np.float64)) valr = np.array([1.5, 2.0]) t_raw = helper.make_tensor(name="raw", data_type=TensorProto.DOUBLE, dims=valr.shape, vals=valr.tobytes(), raw=True) n_bool = onnx.helper.make_node('Cast', inputs=['bool'], outputs=['o_bool'], to=11) n_int8 = onnx.helper.make_node('Cast', inputs=['int8'], outputs=['o_int8'], to=11) n_uint8 = onnx.helper.make_node('Cast', inputs=['uint8'], outputs=['o_uint8'], to=11) n_uint16 = onnx.helper.make_node('Cast', inputs=['uint16'], outputs=['o_uint16'], to=11) n_uint32 = onnx.helper.make_node('Cast', inputs=['uint32'], outputs=['o_uint32'], to=11) n_uint64 = onnx.helper.make_node('Cast', inputs=['uint64'], outputs=['o_uint64'], to=11) node = onnx.helper.make_node( 'Sum', inputs=[ 'o_bool', 'o_int8', 'o_uint8', 'o_uint16', 'o_uint32', 'o_uint64', 'double', 'raw' ], outputs=['out'], ) y = helper.make_tensor_value_info('out', TensorProto.DOUBLE, [2]) return ([n_bool, n_int8, n_uint8, n_uint16, n_uint32, n_uint64, node], [], [y], [ t_bool, t_int8, t_uint8, t_uint16, t_uint32, t_uint64, t_double, t_raw ]) @onnx_test def tan_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10]) node = onnx.helper.make_node( 'Tan', inputs=['x'], outputs=['y'], ) return ([node], [x], [y]) @onnx_test def tanh_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1]) node = onnx.helper.make_node( 'Tanh', inputs=['x'], outputs=['y'], ) return ([node], [x], [y]) @onnx_test def thresholdedrelu_default_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 2, 3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 2, 3]) node = onnx.helper.make_node('ThresholdedRelu', inputs=['x'], outputs=['y']) return ([node], [x], [y]) @onnx_test def thresholdedrelu_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 2, 3]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 2, 3]) alpha = 3.0 node = onnx.helper.make_node('ThresholdedRelu', inputs=['x'], outputs=['y'], alpha=alpha) return ([node], [x], [y]) @onnx_test def thresholdedrelu_int_test(): x = helper.make_tensor_value_info('x', TensorProto.INT32, [2, 2, 3]) y = helper.make_tensor_value_info('y', TensorProto.INT32, [2, 2, 3]) alpha = 3.0 node = onnx.helper.make_node('ThresholdedRelu', inputs=['x'], outputs=['y'], alpha=alpha) return ([node], [x], [y]) @onnx_test def tile_test(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 2]) y = helper.make_tensor_value_info('y', TensorProto.INT64, [2]) z = helper.make_tensor_value_info('z', TensorProto.FLOAT, [2, 4]) node = onnx.helper.make_node('Tile', inputs=['x', 'y'], outputs=['z']) return ([node], [x, y], [z], [helper.make_tensor('y', TensorProto.INT64, [2], [1, 2])]) @onnx_test def tile_test_3x2(): x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 2]) y = helper.make_tensor_value_info('y', TensorProto.INT64, [2]) z = helper.make_tensor_value_info('z', TensorProto.FLOAT, [6, 4]) node = onnx.helper.make_node('Tile', inputs=['x', 'y'], outputs=['z']) return ([node], [x, y], [z], [helper.make_tensor('y', TensorProto.INT64, [2], [3, 2])]) @onnx_test def topk_attrk_test(): x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [2, 5, 3, 2]) val = helper.make_tensor_value_info('val', TensorProto.FLOAT, [2, 2, 3, 2]) ind = helper.make_tensor_value_info('indices', TensorProto.INT64, [2, 2, 3, 2]) node = onnx.helper.make_node('TopK', inputs=['data'], outputs=['val', 'indices'], k=2) return ([node], [x], [val, ind]) @onnx_test def topk_neg_axis_test(): k = np.array([3]) x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 4, 5, 6]) val = helper.make_tensor_value_info('val', TensorProto.FLOAT, [3, 3, 5, 6]) ind = helper.make_tensor_value_info('indices', TensorProto.INT64, [3, 3, 5, 6]) k_tensor = helper.make_tensor(name='k', data_type=TensorProto.INT64, dims=k.shape, vals=k.astype(np.int64)) node = onnx.helper.make_node('TopK', inputs=['data', 'k'], outputs=['val', 'indices'], axis=-2, sorted=0) return ([node], [x], [val, ind], [k_tensor]) @onnx_test def topk_test(): k = np.array([4]) x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [2, 5, 3, 2]) val = helper.make_tensor_value_info('val', TensorProto.FLOAT, [2, 4, 3, 2]) ind = helper.make_tensor_value_info('indices', TensorProto.INT64, [2, 4, 3, 2]) k_tensor = helper.make_tensor(name='k', data_type=TensorProto.INT64, dims=k.shape, vals=k.astype(np.int64)) node = onnx.helper.make_node('TopK', inputs=['data', 'k'], outputs=['val', 'indices'], largest=0, axis=1) return ([node], [x], [val, ind], [k_tensor]) def transpose_default_perm_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 5, 2, 3]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 2, 5, 1]) node = onnx.helper.make_node( 'Transpose', inputs=['0'], outputs=['1'], ) return ([node], [x], [y]) @onnx_test def transpose_invalid_perm_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 2, 4, 3]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 2, 2]) node = onnx.helper.make_node( 'Transpose', perm=[0, 2, 1], inputs=['0'], outputs=['1'], ) return ([node], [x], [y]) @onnx_test def transpose_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 2, 2, 3]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 2, 2]) node = onnx.helper.make_node( 'Transpose', perm=[0, 3, 1, 2], inputs=['0'], outputs=['1'], ) return ([node], [x], [y]) @onnx_test def transpose_gather_test(): x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 5, 4, 6]) i = helper.make_tensor_value_info('indices', TensorProto.INT32, [2, 4, 3, 5]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 2, 3, 4, 5, 4, 5, 6]) td = onnx.helper.make_node( 'Transpose', inputs=['data'], outputs=['tdata'], perm=[0, 2, 1, 3], ) ti = onnx.helper.make_node('Transpose', inputs=['indices'], outputs=['tindices'], perm=[0, 2, 1, 3]) node = onnx.helper.make_node( 'Gather', inputs=['tdata', 'tindices'], outputs=['y'], axis=1, ) return ([td, ti, node], [x, i], [y]) @onnx_test def undefined_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2, 3, 4, 5]) node = onnx.helper.make_node('Identity', inputs=[''], outputs=['1']) return ([node], [x], [y]) @onnx_test def unknown_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4]) helper.make_tensor_value_info('2', TensorProto.FLOAT, [2, 3, 4, 5]) a = helper.make_tensor_value_info('3', TensorProto.FLOAT, [2, 3, 4, 5]) node = onnx.helper.make_node('Unknown', inputs=['0', '1'], outputs=['2']) node2 = onnx.helper.make_node('Unknown', inputs=['2'], outputs=['3']) return ([node, node2], [x, y], [a]) @onnx_test def unknown_aten_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4]) helper.make_tensor_value_info('2', TensorProto.FLOAT, [2, 3, 4, 5]) a = helper.make_tensor_value_info('3', TensorProto.FLOAT, [2, 3, 4, 5]) node = onnx.helper.make_node('ATen', inputs=['0', '1'], outputs=['2'], operator='unknown') return ([node], [x, y], [a]) @onnx_test def upsample_linear_test(): scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) scales_tensor = helper.make_tensor(name='scales', data_type=TensorProto.FLOAT, dims=scales.shape, vals=scales.flatten().astype( np.float32)) X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [1, 1, 2, 2]) Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, []) node = onnx.helper.make_node('Upsample', inputs=['X', '', 'scales'], outputs=['Y'], mode='linear') return ([node], [X], [Y], [scales_tensor]) @onnx_test def upsample_test(): scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32) scale_tensor = helper.make_tensor(name='scales', data_type=TensorProto.FLOAT, dims=scales.shape, vals=scales.flatten().astype(np.float32)) X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [1, 1, 2, 2]) Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [1, 1, 4, 6]) node = onnx.helper.make_node( 'Upsample', inputs=['X', 'scales'], outputs=['Y'], mode='nearest', ) return ([node], [X], [Y], [scale_tensor]) @onnx_test def variable_batch_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [None, 3, 16, 16]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, 3, 16, 16]) node = onnx.helper.make_node('Identity', inputs=['0'], outputs=['1']) return ([node], [x], [y]) @onnx_test def variable_batch_leq_zero_test(): x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [0, 3, 16, 16]) y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [-1, 3, 16, 16]) z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [-1, 3, 16, 16]) node = onnx.helper.make_node('Add', inputs=['0', '1'], outputs=['2']) return ([node], [x, y], [z]) @onnx_test def where_test(): c = helper.make_tensor_value_info('c', TensorProto.BOOL, [2]) x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 2, 2]) y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 1, 2, 2]) z = helper.make_tensor_value_info('z', TensorProto.FLOAT, [2, 2, 2, 2]) node = onnx.helper.make_node('Where', inputs=['c', 'x', 'y'], outputs=['z']) return ([node], [c, x, y], [z])